Epidemiology and Medical statistics Books

Book SynopsisEmerging Infectious Diseases Emerging Infectious Diseases offers an introduction to emerging and reemerging infectious disease, focusing on significant illnesses found in various regions of the world. Many of these diseases strike tropical regions or developing countries with particular virulence, others are found in temperate or developed areas, and still other microbes and infections are more indiscriminate. This volume includes information on the underlying mechanisms of microbial emergence, the technology used to detect them, and the strategies available to contain them. The author describes the diseases and their causative agents that are major factors in the health of populations the world over. The book contains up-to-date selections from infectious disease journals as well as information from the Centers for Disease Control and Prevention, the World Health Organization, MedLine Plus, and the American Society for Microbiology. Perfect for studenTable of ContentsTables and Figures vii Preface xv The Author xvii Acknowledgments xviii Part 1: Introduction to Emerging Infectious Diseases Chapter 1: Infectious Diseases Past and Present 3 Major Concepts 4 • History of Infectious Diseases 5 • The Role of Infectious Diseases in the World Today 8 • The Links Between Infectious Diseases, Poverty, and Civil Unrest 10 • Emerging and Reemerging Infectious Diseases 12 • Factors Contributing to the Emergence of New Infectious Diseases and the Spread and Evolution of Older Diseases 15 • Timeline 18 Chapter 2: Of Microbes and Men 27 Major Concepts 28 • Introduction 30 • Infectious Agents: The Enemy Combatants 30 • Genetic Information and the Making of Proteins: Preparing the Armament 36 • The Immune Response: Humans Fight Back, Part One 40 • Antimicrobial Agents: Humans Fight Back, Part Two 46 Part 2: Bacterial Infections Chapter 3: Lyme Disease 55 Major Concepts 56 • Introduction 58 • History 60 • The Disease 61 • The Causative Agent 63 • The Immune Response 66 • Diagnosis 67 • Treatment 68 • Prevention 69 • Surveillance 71 Chapter 4: Human Ehrlichiosis 75 Major Concepts 76 • Introduction 77 • History 78 • The Diseases 79 • The Causative Agents 88 • The Immune Response 89 • Diagnosis 90 • Treatment 91 • Prevention 92 • Surveillance 92 Chapter 5: Bartonella Infections 97 Major Concepts 98 • Introduction 99 • History 99 • The Diseases 101 • The Causative Agents 106 • The Immune Response 109 • Diagnosis 110 • Treatment 111 • Prevention 111 • Surveillance 112 Chapter 6: Group A Streptococci 117 Major Concepts 118 • Introduction 119 • History 120 • The Diseases 121 • The Causative Agents 127 • The Immune Response 131 • Diagnosis 132 • Treatment 133 • Prevention 134 • Surveillance 135 Chapter 7: Escherichia coli O157:H7 139 Major Concepts 140 • Introduction 141 • History 142 • The Diseases 143 • The Causative Agents 145 • The Immune Response 151 • Diagnosis 152 • Treatment 153 • Prevention 153 • Surveillance 155 Chapter 8: Helicobacter pylori, Ulcers, and Cancer 161 Major Concepts 162 • Introduction 163 • History 164 • The Diseases 165 • The Causative Agent 168 • The Immune Response 171 • Diagnosis 172 • Treatment 173 • Prevention 174 • Surveillance 175 Chapter 9: Legionnaires’ Disease and Pontiac Fever 181 Major Concepts 182 • Introduction 183 • History 184 • The Diseases 185 • The Causative Agent 186 • The Immune Response 191 • Diagnosis 194 • Treatment 197 • Prevention 197 • Surveillance 199 Chapter 10: Pulmonary Tuberculosis and Multidrug Resistance 205 Major Concepts 206 • Introduction 207 • History 208 • The Disease 209 • The Causative Agents 212 • The Immune Response 213 • Detection and Diagnosis 214 • Treatment and Drug Resistance 216 • Prevention 219 • Surveillance 220 Chapter 11: Emerging Bacterial Drug Resistance 225 Major Concepts 226 • Introduction 227 • History 228 • The Diseases, Causative Agents, and Treatment Options 229 • Mechanisms of Resistance 235 • Diagnosis 239 • Prevention 240 • Surveillance 240 Part 3: Viral Infections Chapter 12: Marburg and Ebola Hemorrhagic Fevers 247 Major Concepts 248 • Introduction 249 • History 250 • The Diseases 254 • The Causative Agents 257 • The Immune Response 262 • Diagnosis 263 • Treatment 264 • Prevention 264 • Surveillance 267 Chapter 13: American Hemorrhagic Fevers 273 Major Concepts 274 • Introduction 275 • History 277 • The Diseases 278 • The Causative Agents 282 • The Immune Response 285 • Diagnosis 286 • Treatment 287 • Prevention 287 • Surveillance 288 Chapter 14: Lassa Hemorrhagic Fever 293 Major Concepts 294 • Introduction 295 • History 296 • The Disease 298 • The Causative Agent 300 • The Immune Response 303 • Diagnosis 304 • Treatment 304 • Prevention 306 • Surveillance 308 Chapter 15: Dengue Fever and Dengue Hemorrhagic Fever 313 Major Concepts 314 • Introduction 315 • History 316 • The Diseases 318 • The Causative Agent 321 • The Immune Response 324 • Diagnosis 327 • Treatment 327 • Prevention 328 • Surveillance 329 Chapter 16: The Human Immunodeficiency Virus and Acquired Immune Deficiency Syndrome 335 Major Concepts 336 • Introduction 338 • History 339 • The Diseases 341 • The Causative Agent 344 • The Immune Response 350 • Diagnosis and Detection 355 • Treatment 355 • Prevention 357 • Surveillance 358 Chapter 17: Human Herpesvirus 8 and Kaposi’s Sarcoma 365 Major Concepts 366 • Introduction 367 • History 368 • The Diseases 368 • The Causative Agent 373 • The Immune Response 378 • Diagnosis 379 • Treatment 380 • Prevention 383 • Surveillance 383 Chapter 18: Hepatitis C 389 Major Concepts 390 • Introduction 391 • History 392 • The Diseases 392 • The Causative Agent 397 • The Immune Response 399 • Diagnosis 401 • Treatment 402 • Prevention 403 • Surveillance 404 Chapter 19: Epidemic and Pandemic Influenza 409 Major Concepts 410 • Introduction 411 • History 412 • The Disease 414 • The Causative Agent 415 • The Immune Response 421 • Diagnosis 421 • Treatment 422 • Prevention 422 • Surveillance 425 Chapter 20: Hantavirus Pulmonary Syndrome 431 Major Concepts 432 • Introduction 433 • History 435 • The Diseases 435 • The Causative Agents 439 • The Immune Response 444 • Diagnosis 445 • Treatment 446 • Prevention 447 • Surveillance 450 Chapter 21: Severe Acute Respiratory Syndrome 455 Major Concepts 456 • Introduction 457 • History 457 • The Disease 460 • The Causative Agent 461 • The Immune Response 464 • Diagnosis 465 • Treatment 466 • Prevention 467 • Surveillance 469 Chapter 22: West Nile Disease in the United States 475 Major Concepts 476 • Introduction 477 • History 477 • The Diseases 481 • The Causative Agent 484 • The Immune Response 487 • Diagnosis 488 • Treatment 489 • Prevention 490 • Surveillance 493 Chapter 23: Monkeypox 499 Major Concepts 500 • Introduction 501 • History 502 • The Disease 505 • The Causative Agent 508 • The Immune Response 511 • Diagnosis 513 • Treatment 514 • Prevention 514 • Surveillance 516 Part 4: Parasitic Infections Chapter 24: Malaria: Reemergence and Recent Successes 523 Major Concepts 524 • Introduction 526 • History 526 • The Disease 528 • The Causative Agents 529 • The Immune Response 533 • Diagnosis 535 • Treatment and Drug Resistance 535 • Prevention: Failures and Successes 537 • Surveillance 541 Chapter 25: Babesiosis 547 Major Concepts 548 • Introduction 549 • History 549 • The Disease 550• The Causative Agent 553 • The Immune Response 557 • Diagnosis 558 • Treatment 558 • Prevention 560 • Surveillance 560 Chapter 26: Cryptosporidiosis 565 Major Concepts 566 • Introduction 567 • History 568 • The Disease 569 • The Causative Agents 570 • The Immune Response 576 • Diagnosis 577 • Treatment 578 • Prevention 580 • Surveillance 581 Chapter 27: Chagas’ Disease and Its Emergence in the United States 585 Major Concepts 586 • Introduction 587 • History 587 • The Disease 588 • The Causative Agent 591 • The Immune Response 595 • Diagnosis 598 • Treatment 599 • Prevention 600 • Surveillance 601 Part 5: Infectious Proteins Chapter 28: Creutzfeldt-Jakob Disease and Other Transmissible Spongiform Encephalopathies 609 Major Concepts 610 • Introduction 611 • History 612 • The Diseases 613 • The Causative Agents 621 • The Immune Response 625 • Diagnosis 626 • Treatment 627 • Prevention 628 • Surveillance 628 Part 6: Special Issues in Infectious Diseases Chapter 29: The Emerging Importance of Infectious Diseases in the Immunosuppressed 635 Major Concepts 636 • Introduction 637 • Immunosuppressed Populations 637 • Selected Causes of Immunosuppression 638 • Infectious Diseases of the Immunosuppressed 642 Chapter 30: The Emerging Threat of Bioweapons 667 Major Concepts 668 • Introduction 669 • History 670 • Bioterrorism Agents and Diseases 671 • The Threat of Agroterrorism 692 • Preparation for Biological Attacks 693 • Protective Vaccines 694 Glossary 701 Index 723

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Book SynopsisThis book offers an important reference source about the most common classes of pesticides for researchers engaged in the area of neurotoxicology, metabolism, and epidemiology. The book presents details about thorough characterization of target and non-target enzymes and proteins involved in toxicity and metabolism; and epidemiology of poisonings and fatalities in people from short- and long- term exposures to these pesticides in different occupational settings on an individual country basis as well as on a global basis. The early portion of the book deals with metabolism, mechanisms and biomonitoring of anticholinesterase pesticides, while the later part deals with epidemiological studies, regulatory issues, and therapeutic intervention.Table of ContentsForeword (Donald J. Ecobichon). Section I. 1. Introduction (Tetsuo Satoh, Ramesh C. Gupta). Section II: Metabolism and Mechanisms. 2. ACETYLCHOLINESTERASE AND ACETYLCHOLINE RECEPTORS: BRAIN REGIONAL HETEROGENEITY (Haruo Kobayashi, Tadahiko Suzuki, Fumiaki Akahori and Tetsuo Satoh). 3. GENOMIC IMPLICATIONS OF ANTICHOLINESTERASE SENSITIVITIES (Jonathan E. Cohen, Gabrial Zimmermann, Alon Friedman and Hermona Soreq). 4. BUTYRYLCHOLINESTERASE: OVERVIEW, STRUCTURE AND FUNCTION (Oksana Lockridge, Ellen G. Duysen and Patrick Masson). 5.CARBOXYLESTERASES:OVERVIEW, STRUCTURE, FUNCTION AND POLYMORPHISM (Masakiyo Hosokawa and Tetsuo Satoh). 6. CARBOXYLESTERASES IN THE METABOLISM AND TOXICITY OF PESTICIDES (Colin J. Jackson, Juan Sanchez-Hernandez, Craig E. Wheelock and John G. Oakeshott). 7. THE METABOLIC ACTIVATION AND DETOXICATION OF ANTICHOLINESTERASE INSECTICIDES (Janice E. Chambers, Edward C. Meek and Matthew Ross). 8. PARAOXONASE 1: STRUCTURE, FUNCTION AND POLYMORPHISMS (Lucio G. Costa, Clement E. Furlong). 9. LONG-TERM NEUROTOXICOLOGICAL EFFECTS OF ANTICHOLINESTERASES AFTER EITHER ACUTE AND CHRONIC EXPOSURE (Angelo Moretto, Manuela Tiramani and Claudio Colosio). 10. MOLECULAR TOXICOLOGY OF NEUROPATHY TARGET ESTERASE (Yi-Jun Wu and Ping-An Chang). 11. DETOXICATION OF ANTICHOLINESTERASE PESTICIDES (Miguel A. Sogorb and Eugennio Vilanova). Section III: Toxicity and Biomonitoring. 12. INVOLVEMENT OF OXIDATIVE STRESS IN ANTICHOLINESTERASE PESTICIDES TOXICITY (Dejan Milatovic, Ramesh C. Gupta, Snjezana Zaja-Milanovic, Gregory Barners and Michael Aschner). 13.CENTRAL MECHANISMS OF SEIZURES AND LETHALITY FOLLOWING ANTICHOLINESTERASE PESTICIDE EXPOSURE (Andrzej Dekundy and Rafal M. Kaminski). 14. APOPTOSIS INDUCED BY ANTICHOLINESTERASE PESTICIDES (Qing Li). 15. GENE EXPRESSION (Shirin Pournourmahammadi and Mohammad Abdollahi). 16. ORGANOPHOSPHATES AS ENDOCRINE DISRUPTORS (Shigeyuki Kitamura, Kazumi Sugihara, Nariaki Fujimoto and Takeshi Yamazaki). 17. DEVELOPMENTAL NEUROTOXICITY OF ANTICHOLINESTERASE PESTICIDES (John Flaskos and Magdalini Sachana). 18.TOXICITY OF ANTICHOLINESTERASE PESTICIDES IN NEONATES AND CHILDREN (Diane Rohlman and Linda McCauley). 19. NEUROTOXICITY OF ORGANOPHOSPHATES AND CARBAMATES (Kiran Dip Gill, Govinder Flora and Swaran J.S. Flora). 20. BIOMONITORING OF PESTICIDES: PHARMACOKINETICS OF ORGANOPHOSPHORUS AND CARBAMATE INSECTICIDES (Charles Timchalk). 21. NOVEL BIOMARKERS OF ORGANOPHOSPHATE EXPOSURE (Tetsuo Satoh, Salmaan H. Inayat-Hussain, Michihiro Kamishima and Jun Ueyama). 22. BIOMARKERS OF CARCINOGENESIS IN RELATION TO PESTICIDES POISONING (Manashi Bagchi, Shirley Zafra-Stone, Francis C. Lau and Debasis Bagchi). 23. ANTICHOLINESTERASE PESTICIDES INTERACTION (Ramesh C. Gupta and Dejan Milatovic). 24. INTERACTION OF ANTICHOLINESTERASE PESTICIDES WITH METALS (Jitendra K. Malik, Avinash G. Telang, Ashok Kumar and Ramesh C. Gupta). Section IV: Epidemiological studies. 25. GLOBAL IMPACT (Claudio Colosio, Francesca Vellere and Angelo Moretto). 26. CHILE (Floria Pancetti, Muriel Ramirez and Mauricio Castillo). 27. CHINA (Yueming Jiang). 28. EGYPT (Sameeh A. Mansour). 29. GREECE (Maria Stefanidou, S. Athanaselis, C. Spiliopoulou and C. Maravelias). 30. INDIA (Pawan K. Gupta). 31. IRAN (Mohammad Abdollahi). 32. ISRAEL (Yoram Finkelstein). 33. JAPAN (Takemi Yoshida and Yumiko Kuroki). 34. KOREA (Hyung-Keun RoBum Jin Oh, Mi-Jin Lee and Joo-Hyun Suh). 35. MEXICO (Betzabet Quintanilla-Vega, Norma Pérez-Herrera and Elizabeth Rojas-Garcia). 36. SERBIA (Milan Jokanović, Biljana Antonijević and Slavica Vučinić). 37. SPAIN (Antonio F. Hernández, Tesifón Parrón, José L. Serrano and Porfirio Marín, on behalf of the ESPAPP group). 38. TAIWAN (Tzeng Jih Lin,Dong-Zeng Hung, Jin-Lian Tsai, Sheng-Chuan Hu and Jou-Fang Deng). 39. THAILAND (Winai Wananukul). 40. TURKEY (Ismet COK). 41. U.S.A. (Anna M. Fan). Section V. 42. Regulatory Aspects (Kai Savolainen). Section VI. 43. Medical Treatment of Poisoning with Organophosphates and Carbamates (Milan Jokanović).

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Book SynopsisThe only book available in the area of forward-time population genetics simulationsapplicable to both biomedical and evolutionary studies The rapid increase of the power of personal computers has led to the use of serious forward-time simulation programs in genetic studies. Forward-Time Population Genetics Simulations presents both new and commonly used methods, and introduces simuPOP, a powerful and flexible new program that can be used to simulate arbitrary evolutionary processes with unique features like customized chromosome types, arbitrary nonrandom mating schemes, virtual subpopulations, information fields, and Python operators. The book begins with an overview of important concepts and models, then goes on to show how simuPOP can simulate a number of standard population genetics modelswith the goal of demonstrating the impact of genetic factors such as mutation, selection, and recombination on standard Wright-Fisher models. The rest of the book is devoted to aTable of ContentsPreface ix Acknowledgments xiii List of examples xxiii 1. Basic concepts and models 1 1.1 Biological and genetic concepts 2 1.2 Population and evolutionary genetics 6 1.3 Statistical genetics and genetic epidemiology 17 2. Simulation of population genetics models 25 2.1 Random genetic drift 25 2.2 Demographic models 29 2.3 Mutation 31 2.4 Migration 34 2.5 Recombination and linkage disequilibrium 36 2.6 Natural selection 37 2.7 Genealogy of forward-time simulations 41 3. Ascertainment bias in population genetics 47 3.1 Introduction 47 3.2 Methods 49 3.3 Results 54 3.4 Discussion and Conclusions 58 4. Observing properties of evolving populations 63 4.1 Introduction 64 4.2 Simulation of the evolution of allele spectra 66 4.3 Extensions to the basic model 78 5. Simulating populations with complex human diseases 89 5.1 Introduction 89 5.2 Controlling disease allele frequencies at the present generation 91 5.3 Forward-time simulation of realistic samples 102 5.4 Discussion 119 6. Nonrandom mating and its applications 125 6.1 Assortative mating 126 6.2 More complex non-random mating schemes 132 6.3 Hetergeneous mating schemes 140 6.4 Simulation of age structured populations 145 Appendix: Forward-time simulations using stimulPOP 157 A.1 Introduction 157 A.2 Population 160 A.3 Operators 172 A.4 Evolve on or more populations 181 A.5 A complete stimuPOP script 185

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Book SynopsisProvides timely applications, modifications, and extensions of experimental designs for a variety of disciplines Design and Analysis of Experiments, Volume 3: Special Designs and Applications continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research. Featuring contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes: Trade Review “The presentation by Wiley is superb, as we have come to expect. All in all, this is a first class offering.”( International Statistical Review, 1 October 2012) Table of ContentsPreface xvii Contributors xxi 1 Genetic Crosses Experiments 1 Murari Singh, Sudhir Gupta, and Rajender Parsad 1.1 Introduction, 1 1.2 Basic Objectives and Models, 2 1.3 Diallel Mating Design of Type I, 8 1.4 Diallel Crosses: Type II Designs, 14 1.5 Partial Diallel Crosses: No Blocking or Complete Blocks, 25 1.6 Partial Diallel Crosses in Incomplete Blocks, 32 1.7 Optimality, 44 1.8 Robustness, 59 1.9 Three- or Higher-Way Crosses, 61 1.10 Computation, 65 2 Design of Gene Expression Microarray Experiments 73 Dan Nettleton 2.1 Introduction, 73 2.2 Gene Expression Microarray Technology, 74 2.3 Preprocessing of Microarray Fluorescence Intensities, 76 2.4 Introduction to Gene Expression Microarray Experimental Design, 80 2.5 Two-Treatment Experiments Using Two-Color Microarrays, 81 2.6 Two-Color Microarray Experiments Involving More Than Two Treatments, 86 2.7 Multifactor Two-Color Microarray Experiments, 89 2.8 Phase 2 Designs for Complex Phase 1 Designs, 94 3 Spatial Analysis of Agricultural Field Experiments 109 Joanne K. Stringer, Alison B. Smith, and Brian R. Cullis 3.1 Introduction, 109 3.2 Methods to Account for Spatial Variation, 110 3.3 A Spatial Linear Mixed Model, 116 3.4 Analysis of Examples, 122 4 Optimal Designs for Generalized Linear Models 137 John Stufken and Min Yang 4.1 Introduction, 137 4.2 Notation and Basic Concepts, 141 4.3 Tools for Finding Locally Optimal Designs, 145 4.4 GLMs with Two Parameters, 149 4.5 GLMs with Multiple Parameters, 155 4.6 Summary and Concluding Comments, 161 5 Design and Analysis of Randomized Clinical Trials 165 Janet Wittes and Zi-Fan Yu 5.1 Overview, 165 5.2 Components of a Randomized Clinical Trial, 168 5.3 Bias, 175 5.4 Statistical Analysis of Randomized Clinical Trials, 182 5.5 Failure Time Studies, 184 5.6 Other Topics, 206 6 Monitoring Randomized Clinical Trials 213 Eric S. Leifer and Nancy L. Geller 6.1 Introduction, 213 6.2 Normally Distributed Outcomes, 215 6.3 Brownian Motion Properties, 217 6.4 Brief Historical Overview of Group Sequential Methods, 219 6.5 Dichotomous Outcomes, 223 6.6 Time-to-Event Outcomes, 225 6.7 Unconditional Power, 227 6.8 Conditional Power, 229 6.9 Spending Functions, 232 6.10 Flexibility and Properties of Spending Functions, 233 6.11 Modifying the Trial’s Sample Size Based on a Nuisance Parameter, 235 6.12 Sample Size Modification Based on the Interim Treatment Effect, 240 6.13 Concluding Remarks, 246 7 Adaptive Randomization in Clinical Trials 251 Lanju Zhang and William F. Rosenberger 7.1 Introduction, 251 7.2 Adaptive Randomization Procedures, 252 7.3 Likelihood-Based Inference, 264 7.4 Randomization-Based Inference, 269 7.5 Conclusions and Practical Considerations, 276 8 Search Linear Model for Identification and Discrimination 283 Subir Ghosh 8.1 Introduction, 283 8.2 General Linear Model with Fixed Effects, 284 8.3 Search Linear Model, 285 8.4 Applications, 288 8.5 Effects of Noise in Performance Comparison, 293 9 Minimum Aberration and Related Criteria for Fractional Factorial Designs 299 Hegang H. Chen and Ching-Shui Cheng 9.1 Introduction, 299 9.2 Projections of Fractional Factorial Designs, 302 9.3 Estimation Capacity, 304 9.4 Clear Two-Factor Interactions, 307 9.5 Estimation Index, 310 9.6 Estimation Index, Minimum Aberration, and Maximum Estimation Capacity, 314 9.7 Complementary Design Theory for Minimum Aberration Designs, 315 9.8 Nonregular Designs and Orthogonal Arrays, 317 9.9 Generalized Minimum Aberration, 320 9.10 Optimal Fractional Factorial Block Designs, 322 10 Designs for Choice Experiments for the Multinomial Logit Model 331 Deborah J. Street and Leonie Burgess 10.1 Introduction, 331 10.2 Definitions, 332 10.3 The MNL Model, 335 10.4 Design Comparisons, 338 10.5 Optimal Designs for DCEs, 340 10.6 Using Combinatorial Designs to Construct DCEs, 364 10.7 Bayesian Work, 368 10.8 Best–Worst Experiments, 368 10.9 Miscellaneous Topics, 370 11 Computer Experiments 379 Max D. Morris 11.1 Introduction, 379 11.2 Sensitivity/Uncertainty Analysis, 382 11.3 Gaussian Stochastic Process Models, 385 11.4 Inference, 389 11.5 Experimental Designs, 398 11.6 Multivariate Output, 403 11.7 Multiple Data Sources, 406 11.8 Conclusion, 409 12 Designs for Large-Scale Simulation Experiments, with Applications to Defense and Homeland Security 413 Susan M. Sanchez, Thomas W. Lucas, Paul J. Sanchez, Christopher J. Nannini, and Hong Wan 12.1 Introduction, 413 12.2 Philosophy: Evolution of Computational Experiments, 414 12.3 Application: U.S. Army Unmanned Aerial Vehicle (UAV) Mix Study, 422 12.4 Parting Thoughts, 437 13 Robust Parameter Designs 443 Timothy J. Robinson and Christine M. Anderson-Cook 13.1 Introduction, 443 13.2 Taguchi Signal-to-Noise Ratio Approach, 445 13.3 Dual Model Response Surface Methodology, 448 13.4 Single Model Response Surface Methods Using Combined Arrays, 451 13.5 Computer Generated Combined Arrays, 461 13.6 RPD Involving Quantitative and Qualitative Factors, 465 13.7 Conclusions, 466 14 Split-Plot Response Surface Designs 471 G. Geoffrey Vining 14.1 Introduction, 471 14.2 Differences between Agricultural and Industrial Experimentation, 472 14.3 OLS–GLS Equivalent Second-Order Split-Plot Designs and Analysis, 482 14.4 Exact Tests for the Coeffi cients, 488 14.5 Proper Residuals for Checking Assumptions, 493 14.6 "Optimal" Second-Order Split-Plot Designs, 496 15 Design and Analysis of Experiments for Directional Data 501 Sango B. Otieno and Christine M. Anderson-Cook 15.1 Summary, 501 15.2 Introduction and Historical Background, 501 15.3 ANOVA for Circular Data, 509 15.4 ANOVA for Cylindrical Data, 521 15.5 ANOVA for Spherical Data, 524 15.6 Conclusions, 530 References, 531 Author Index 533 Subject Index 545

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Book Synopsis* Provides a comprehensive, in-depth treatment of statistical methods in healthcare. * Presents a reference source for practitioners and specialists in health care and drug development. * Offers a broad coverage of standards and established methods through leading edge techniques.Table of ContentsForeword xix Preface xxi Editors xxiii Contributors xxv Part One STATISTICS IN THE DEVELOPMENT OF PHARMACEUTICAL PRODUCTS 1 Statistical Aspects in ICH, FDA and EMA Guidelines 3Allan Sampson and Ron S. Kenett 2 Statistical Methods in Clinical Trials 22Telba Irony, Caiyan Li and Phyllis Silverman 3 Pharmacometrics in Drug Development 56Serge Guzy and Robert Bauer 4 Interactive Clinical Trial Design 78Zvia Agur 5 Stage-wise Clinical Trial Experiments in Phases I, II and III 103Shelemyahu Zacks 6 Risk Management in Drug Manufacturing and Healthcare 122Ron S. Kenett 7 The Twenty-first Century Challenges in Drug Development 155Yafit Stark Part Two STATISTICS IN OUTCOMES ANALYSIS 8 The Issue of Bias in Combined Modelling and Monitoring of Health Outcomes 169Olivia A. J. Grigg 9 Disease Mapping 185Annibale Biggeri and Dolores Catelan 10 Process Indicators and Outcome Measures in the Treatment of Acute Myocardial Infarction Patients 219Alessandra Guglielmi, Francesca Ieva, Anna Maria Paganoni and Fabrizio Ruggeri 11 Meta-analysis 230Eva Negri Part Three STATISTICAL PROCESS CONTROL IN HEALTHCARE 12 The Use of Control Charts in Healthcare 253William H. Woodall, Benjamin M. Adams and James C. Benneyan 13 Common Challenges and Pitfalls Using SPC in Healthcare 268Victoria Jordan and James C. Benneyan 14 Six Sigma in Healthcare 286Shirley Y. Coleman 15 Statistical Process Control in Clinical Medicine 309Per Winkel and Nien Fan Zhang Part Four APPLICATIONS TO HEALTHCARE POLICY AND IMPLEMENTATION 16 Modeling Kidney Allocation: A Data-driven Optimization Approach 335Inbal Yahav 17 Statistical Issues in Vaccine Safety Evaluation 353Patrick Musonda 18 Statistical Methods for Healthcare Economic Evaluation 365Caterina Conigliani, Andrea Manca and Andrea Tancredi 19 Costing and Performance in Healthcare Management 386Rosanna Tarricone and Aleksandra Torbica Part Five APPLICATIONS TO HEALTHCARE MANAGEMENT 20 Statistical Issues in Healthcare Facilities Management 407Daniel P. O'Neill and Anja Drescher 21 Simulation for Improving Healthcare Service Management 426Anne Shade 22 Statistical Issues in Insurance/payor Processes 445Melissa Popkoski 23 Quality of Electronic Medical Records 456Dario Gregori and Paola Berchialla References 475 Index 481

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Book SynopsisThis new title in the World Psychiatric Association Series offers insightful commentaries from experts in PTSD. With a critical review of the evidence on causes and treatment, this concise volume will be useful to both psychiatrists and clinical psychologists as well as trainees.Trade Review“This is a valuable contribution to the psychiatric literature, helping clinicians, researchers, and policymakers focus their attention on the ongoing changes in the diagnosis, treatment, prognosis, and epidemiology of a known disorder.” (Doody’s, 13 April 2012) “The book, however, will provide a solid reference for clinicians and practitioners and is recommended.” (PsycCRITIQUES, 28 March 2012) "At the same time, there is enormous value in exploring one topic very well, and this text certainly offers trauma researchers and practitioners an authoritative synthesis and evaluation of knowledge about PTSD itself." (University of Cape Town Child Guidance Clinic, 2012)Table of ContentsPreface xi List of Contributors xv 1 PTSD and Related Disorders 1 Matthew J. Friedman Commentaries 1.1 Walking the Line in Defining PTSD: Comprehensiveness Versus Core Features 35 Chris R. Brewin 1.2 Trauma-Related Disorders in the Clinical and Legal Settings 38 Elie G. Karam 1.3 Redefining PTSD in DSM-5: Conundrums and Potentially Unintended Risks 42 Alexander C. McFarlane 2 Epidemiology of PTSD 49 Carlos Blanco Commentaries 2.1 Challenges and Future Horizons in Epidemiological Research into PTSD 75 Abdulrahman M. El-Sayed and Sandro Galea 2.2 Preventing Mental Ill-Health Following Trauma 79 Helen Herrman 2.3 PTSD Epidemiology with Particular Reference to Gender 82 Marianne Kastrup 3 Neurobiology of PTSD 89 Arieh Y. Shalev, Asaf Gilboa and Ann M. Rasmusson Commentaries 3.1 Translational Theory-Driven Hypotheses and Testing Are Enhancing Our Understanding of PTSD and its Treatment 139 Brian H. Harvey 3.2 Precipitating and design approaches to PTSD 142 Eric Vermetten 4 Pharmacotherapy of PTSD 149 Dan J. Stein and Jonathan C. Ipser Commentaries 4.1 Critical View of the Pharmacological Treatment of Trauma 163 Marcelo F. Mello 4.2 Shortcomings and Future Directions of the Pharmacotherapy of PTSD 164 Michael Van Ameringen and Beth Patterson 4.3 Dire Need for New PTSD Pharmacotherapeutics 167 Murray B. Stein 5 Psychological Interventions for Trauma Exposure and PTSD 171 Richard A. Bryant Commentaries 5.1 Psychological Interventions for PTSD in Children 203 Lucy Berliner 5.2 Challenges in the Dissemination and Implementation of Exposure-Based CBT for the Treatment of Hispanics with PTSD 205 Rafael Kichic, Mildred Vera, and Marıa L. Reyes-Rabanillo 5.3 What Else Do We Need to Know about Evidence-Based Psychological Interventions for PTSD? 208 Karina Lovell 5.4 Another Perspective on Exposure Therapy for PTSD 211 Barbara Olasov Rothbaum 6 (Disaster) Public Mental Health 217 Joop de Jong Commentaries 6.1 An Excellent Model for Low- and Middle-Income Countries 263 Dean Ajdukovic 6.2 Disaster Mental Health and Public Health: An Integrative Approach to Recovery 266 Suresh Bada Math, Channaveerachari Naveen Kumar and Maria Christine Nirmala 6.3 Transcultural Aspects of Response to Disasters 272 Tarek A. Okasha 6.4 Disaster Public Health: Health Needs, Psychological First Aid and Cultural Awareness 275 Robert J. Ursano, Matthew N. Goldenberg, Derrick Hamaoka and David M. Benedek Index 281

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Book SynopsisDuring the last twenty years statistical methodology has become of central importance in research studies in medicine and also in day-to-day clinical practice.Table of ContentsForeword. Preface. Biographical Information on the Editors. List of Contributors. Abbreviations and Acronyms. The Encyclopaedic Companion to Medical Statistics.

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Book SynopsisThe new edition of this critically praised textbook continues to provide the most comprehensive overview of the concepts, methods, and research advances in the field; particularly the application of molecular genomics and of neuroimaging. It has been revised and enhanced to capitalize on the strengths of the first and second editions while keeping it up-to-date with the field of psychiatry and epidemiology. This comprehensive publication now includes chapters on experimental epidemiology, gene-environment interactions, the use of case registries, eating disorders, suicide, childhood disorders and immigrant populations, and the epidemiology of a number of childhood disorders. As in the first and second editions, the objective is to provide a comprehensive, easy to understand overview of research methods for the non-specialist. The book is ideal for students of psychiatric epidemiology, psychiatric residents, general psychiatrists, and other mental health professionals. The boTrade Review"The Textbook of Psychiatric Epidemiology is a timely, up-to-date, and comprehensive book covering all aspects of the science of epidemiology as related to psychiatric disorders. Overall, this is a well written, wel-organized book that I highly recommend to all psychiatrists and neuroscientists who are interested in the underpinning of psychiatric epidemiology." (Journal of Clinical Psychiatry, 2013) “Overall, this book provides a thorough and understandable introduction to the field of psychiatric epidemiology, suitable for students wishing to become familiar with up-to-date applications and findings in psychiatry as they relate to public health and epidemiology.” (Doody’s, 6 April 2012) "This 3rd edition is a welcome return for this informative textbook, which will be of interest to a wide readership through its extensive scope. It covers a range of areas relevant to epidemiology and beyond with what is now the standard format involving expert authors or teams." (Acta Psychiatrica Scandinavica, 2012) "So I started this review process with a great deal of skepticism. However, during this process I have ended up reading about areas outside my own main research field with much interest, and chapters within my own areas with much pleasure and admiration for the contributions; and, I have found myself recommending individual chapters again and again to students and other people with whom I, during this period, have discussed specific projects. So, much to my own surprise, I end up warmly recommending this book to anyone who has a professional interest in the epidemiology of, and risk factors for psychiatric disorders, and a recurring need for a fast reference to a very comprehensive array of knowledge." Read the full review. (Acta Neuropsychiatrica, 2012) Table of ContentsList of Contributors. 1 Introduction to epidemiologic research methods (Glyn Lewis). 1.1 What is epidemiology? 1.2 Causation in medicine. 1.3 Causal inference. 1.4 The future for psychiatric epidemiology. 2 Analysis of categorical data: The odds ratio as a measure of association and beyond (Garrett M. Fitzmaurice and Caitlin Ravichandran). 2.1 Introduction. 2.2 Inference for a single proportion. 2.3 Analysis of 2 x 2 contingency tables. 2.4 Analysis of sets of 2 x 2 contingency tables. 2.5 Logistic regression. 2.6 Advanced topics. 2.7 Concluding remarks. 2.8 Further reading. 3 Genetic epidemiology (Stephen V. Faraone, Stephen J. Glatt and Ming T. Tsuang). 3.1 Introduction. 3.2 The chain of psychiatric genetic research. 3.3 Psychiatric genetics and psychiatric epidemiology. 4 Examining gene–environment interplay in psychiatric disorders (Judith Allardyce and Jim van Os). 4.1 Introduction. 4.2 The process of genetic epidemiology. 4.3 Gene–environment interplay takes different forms. 4.4 Gene–environment correlation. 4.5 Gene–environment interaction. 4.6 Measurement of genotype, environmental exposure and pathological phenotype. 4.7 Models of GxE. 4.8 Which scale should we use to measure GxE? 4.9 Study designs for the detection of GxE. 4.10 Threats to the validity of epidemiological GxE studies. 4.11 Epigenetic mechanisms. 5 Reliability (Patrick E. Shrout). 5.1 Introduction. 5.2 The reliability coefficient. 5.3 Designs for estimating reliability. 5.4 Statistical remedies for low reliability. 5.5 Reliability theory and binary judgements. 5.6 Reliability statistics: General. 5.7 Other reliability statistics. 5.8 Summary and conclusions. 6 Moderators and mediators: Towards the genetic and environmental bases of psychiatric disorders (Helena Chmura Kraemer). 6.1 Introduction. 6.2 Current methodological barriers. 6.3 Moderation, mediation and other ways in which risk factors 'work together'. 6.4 Extensions. 6.5 Beyond moderators and mediators. 7 Validity: Definitions and applications to psychiatric research (Jill M. Goldstein, Sara Cherkerzian and John C. Simpson). 7.1 Introduction. 7.2 Validity of a construct. 7.3 Validity of the relationships between variables. 7.4 Summary. 8 Use of register data for psychiatric epidemiology in the Nordic countries (Jouko Miettunen, Jaana Suvisaari, Jari Haukka and Matti Isohanni). 8.1 Introduction. 8.2 Registers for use in psychiatric research. 8.3 Register research in Denmark. 8.4 Register research in Finland. 8.5 Register research in Norway. 8.6 Register research in Sweden. 8.7 Discussion. 9 An introduction to mental health services research (Anna Fernandez, Alejandra Pinto-Meza, Antoni Serrano-Blanco, Jordi Alonso and Josep Maria Haro). 9.1 Introduction. 9.2 What is mental health services research? 9.3 A framework for mental health services research. 9.4 Key concepts in mental health services research. 9.5 Examples of mental health services research studies. 9.6 Conclusion. 10 The pharmacoepidemiology of psychiatric medications (Philip S. Wang, Alan M. Brookhart, Christine Ulbricht and Sebastian Schneeweiss). 10.1 Introduction. 10.2 Overview of psychopharmacoepidemiology. 10.3 Sources of data. 10.4 Examples of recent psychopharmacoepidemiologic studies. 10.5 Conclusions. 11 Peering into the future of psychiatric epidemiology (Michaeline Bresnahan, Ezra Susser, Dana March and Bruce Link). 11.1 Introduction. 11.2 Levels of causation: A historical overview. 11.3 Levels of causation. 11.4 Causation over (life) time. 11.5 Examples. 11.6 Framing the future. 12 Studying the natural history of psychopathology (William W. Eaton). 12.1 Introduction. 12.2 Onset. 12.3 Course. 12.4 Outcome. 12.5 Methodological concepts for studying the natural history of psychopathology. 12.6 Conclusion. 13 Symptom scales and diagnostic schedules in adult psychiatry (Jane M. Murphy). 13.1 Introduction. 13.2 North American instruments for epidemiological research. 13.3 North American instruments for psychiatric services and primary care. 13.4 European instruments for psychiatric services and primary care. 13.5 European instruments for epidemiological research. 13.6 Summary. 14 The National Comorbidity Survey (NCS) and its extensions (Ronald C. Kessler). 14.1 Introduction. 14.2 The baseline NCS. 14.3 The NCS follow-up survey (NCS-2). 14.4 The NCS replication survey (NCS-R). 14.5 The NCS-R adolescent supplement (NCS-A). 14.6 The WHO WMH Surveys. 14.7 Overview. 15 Experimental epidemiology (John R. Geddes). 15.1 Introduction. 15.2 Limitations of non-randomised evidence. 15.3 RCTs: The translation of the experimental design into the real world. 15.4 Importance and control of systematic error or bias. 15.5 Importance and control of random error and noise. 15.6 Reporting the results of clinical trials—the CONSORT statement. 15.7 Different clinical questions will prioritise control of different threats to validity and confidence. 15.8 The classification of RCTs. 15.9 Effectiveness trials in schizophrenia. 15.10 Department of Veterans Affairs co-operative study on the cost-effectiveness of Olanzapine (Rosenheck). 15.11 The clinical antipsychotic trials of intervention effectiveness (CATIE) study. 15.12 Cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1). 15.13 European first-episode schizophrenia trial (EUFEST). 15.14 The size and cost of experimental studies in psychiatry. 15.15 Clinical trials in the future. 16 Epidemiology of Schizophrenia (William W. Eaton, Chuan-Yu Chen and Evelyn J. Bromet). 16.1 Introduction. 16.2 Methods. 16.3 The burden of schizophrenia. 16.4 Natural history. 16.5 Demographic correlates. 16.6 Social risk factors. 16.7 Biological risk factors. 16.8 Prevention. 16.9 Discussion. 17 Epidemiology of depressive disorders (Deborah S. Hasin, Miriam C. Fenton and Myrna M. Weissman). 17.1 Introduction. 17.2 Major depression. 17.3 Dysthymia. 17.4 Summary. 18 Epidemiology of anxiety disorders (Ewald Horwath, Felicia Gould and Myrna M. Weissman). 18.1 Introduction. 18.2 Anxiety disorders. 18.3 Panic disorder. 18.4 Agoraphobia. 18.5 Social phobia. 18.6 Generalised anxiety disorder. 18.7 Obsessive–compulsive disorder. 18.8 Anxiety and affective disorders and mass disasters. 18.9 Future developments. 19 Epidemiology of bipolar disorder in adults and children (Kathleen R. Merikangas and Mauricio Tohen). 19.1 Introduction. 19.2 Epidemiology of bipolar disorder. 19.3 Patterns of comorbidity of bipolar disorder. 19.4 Risk Factors. 19.5 Future directions. 19.6 Summary. 20 Epidemiology of eating disorders (Tracey D. Wade, Anna Keski-Rahkonen and James I. Hudson). 20.1 Introduction. 20.2 Case definition. 20.3 Major prevalence studies. 20.4 Incidence studies. 20.5 Comorbidity. 20.6 Mortality from eating disorders. 20.7 Risk factors. 20.8 Future directions. 21 Epidemiology of alcohol use, abuse and dependence (Deborah A. Dawson, Ralph W. Hingson and Bridget F. Grant). 21.1 Introduction. 21.2 Population estimates of per capita consumption. 21.3 Survey-based estimates of the prevalence of drinking. 21.4 Alcohol-related mortality and morbidity. 21.5 Alcohol and injury. 21.6 Alcohol and chronic disease. 21.7 Diagnostic classification of alcohol use disorders. 21.8 Population estimates, prevalence, incidence and natural course of alcohol use disorders. 21.9 Comorbidity of DSM-IV alcohol use disorders and other psychiatric disorders. 21.10 Summary. 22 Epidemiology of illicit drug use disorders (Wilson M. Compton, Marsha F. Lopez, Kevin P. Conway and Yonette F. Thomas). 22.1 Introduction. 22.2 Drug consumption. 22.3 Definitions. 22.4 Rates of DSM-IV abuse and dependence. 22.5 Global rates of drug use disorders. 22.6 Comorbidities with psychiatric conditions. 22.7 Genetic epidemiology. 22.8 Future opportunities. 22.9 Conclusions. 22.10 Disclaimer. 23 The epidemiology of personality disorders: Findings, methods and concepts (Michael J. Lyons, Beth A. Jerskey and Margo R. Genderson). 23.1 Introduction. 23.2 Substantive findings. 23.3 Course, prognosis and developmental issues. 23.4 Treated prevalence. 23.5 Prevalence of specific personality disorders. 23.6 Antisocial personality disorder. 23.7 Conceptual issues. 23.8 Models of personality disorder. 23.9 Methodological issues. 23.10 Future directions. 24 The epidemiology of depression and anxiety in children and adolescents (Kathleen Ries Merikangas and Erin F. Nakamura). 24.1 Introduction. 24.2 Magnitude of depression and anxiety in children and adolescents. 24.3 Correlates and risk factors. 24.4 Service patterns and impact. 24.5 Summary. 25 Epidemiology of attention deficit hyperactivity disorder (Stephen V. Faraone). 25.1 Introduction. 25.2 Prevalence of ADHD. 25.3 Pharmacoeconomics of ADHD. 25.4 Comorbid psychiatric disorders. 25.5 Demographic risk factors. 25.6 Genetic risk factors. 25.7 Environmental risk factors for ADHD. 25.8 Summary and conclusions. 25.9 Future directions. 26 The epidemiology of autism (Gregory S. Liptak). 26.1 Introduction. 26.2 Background. 26.3 Definition and diagnosis. 26.4 Natural history. 26.5 Prevalence. 26.6 Risk factors. 26.7 Genetic factors. 26.8 Public health impact. 26.9 Associations and causal factors. 26.10 Future directions. 26.11 Summary. 27 Mental illness, women, mothers and their children (Kathryn M. Abel and Vera A. Morgan). 27.1 Introduction. 27.2 The epidemiology of mental illness in women of reproductive age. 27.3 Fertility and fecundity in women with mental illness. 27.4 Maternal mental illness at the time of conception and during pregnancy. 27.5 Gene–environment interactions and offspring outcomes. 27.6 Obstetric complications and risk of adult onset mental disorder in offspring. 27.7 Parental condition. 27.8 Motherhood and perinatal mental illness. 27.9 Designing studies examining the relationship between maternal mental illness and outcomes for their children. 27.10 Conclusions. 28 Epidemiology of suicide and attempted suicide(Dianne Currier and Maria A. Oquendo). 28.1 Introduction. 28.2 Definitions. 28.3 Prevalence of suicide and attempted suicide. 28.4 Risk factors for suicide and attempted suicide. 28.5 Protective factors. 28.6 Conclusions. 29 Epidemiology and geriatric psychiatry (Celia F. Hybels and Dan G. Blazer). 29.1 Introduction. 29.2 Issues of case identification. 29.3 The distribution of cases. 29.4 Aetiological studies. 29.5 Outcome studies. 29.6 Historical trends in the epidemiology of psychiatric disorders in late life. 29.7 Use of health care services. 30 Recent epidemiological studies of psychiatric disorders in Japan (Masayoshi Kawai, Kenji J. Tsuchiya and Nori Takei). 30.1 Introduction. 30.2 Schizophrenia. 30.3 Affective disorders. 30.4 Autism and autism spectrum disorder. 30.5 Summary. 31 Epidemiology of migration and serious mental illness: The example of migrants to Europe (Monica Charalambides, Craig Morgan and Robin M. Murray). 31.1 Introduction. 31.2 Defining the constructs. 31.3 High rates of psychosis in migrants: A genuine finding or methodological artefact? 31.4 Possible explanations. 31.5 Biological considerations. 31.6 Cannabis use. 31.7 Adverse social experiences. 31.8 Mechanisms. 31.9 Implications. 32 Epidemiology of migration substance use disorder in Latin American populations and migration to the United States (Marıa Elena Medina-Mora, Guilherme Borges, Tania Real and Jorge Villatoro). 32.1 Introduction. 32.2 Definitions: What do we understand by migration? 32.3 Countries of origin: Social, political and other reasons that trigger migration. 32.4 Living conditions of migrants in the United States. 32.5 Alcohol and drug use in countries of origin and receiving communities. 32.6 Dependence and treatment rates. 32.7 The process of migrating. 32.8 Migration, substance use and access to services. 32.9 Returning migrants and families left behind. 32.10 Conclusions. 33 Early detection and intervention as approaches for preventing schizophrenia (Ming T. Tsuang, William S. Stone, Margo Genderson and Michael Lyons). 33.1 Introduction. 33.2 Modelling genetic and phenotypic heterogeneity. 33.3 Defining a syndrome of liability using cognitive and clinical characteristics of relatives. 33.4 Gene-based vs. genome-based research. 33.5 Future directions. 33.6 Clinical implications. Acknowledgements. References. Index.

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Book SynopsisStatistical science plays an increasingly important role in medical research. Over the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers and, with the appropriate software now easily available, these techniques can be used almost routinely to great effect.Table of ContentsPreface. Prologue. 1. The Generalized Linear Model. 1.1 Introduction. 1.2 The generalized linear model – a brief non-technical account. 1.3 Examples of the application of generalized linear models. 1.4 Poisson regression. 1.5 Overdispersion. 1.6 Summary. 2. Generalized Linear Models for Longitudinal Data. 2.1 Introduction. 2.2 Marginal and conditional regression models. 2.3 Marginal and conditional regression models for continuous responses with Gaussian errors. 2.4 Marginal and conditional regression models for non-normal responses. 2.5 Summary. 3. Missing Values, Drop-outs, Compliance and Intention-to-Treat. 3.1 Introduction. 3.2 Missing values and drop-outs. 3.3 Modelling longitudinal data containing ignorable missing values. 3.4 Non-ignorable missing values. 3.5 Compliance and intention-to-treat. 3.6 Summary. 4. Generalized Additive Models. 4.1 Introduction. 4.2 Scatterplot smoothers. 4.3 Additive and generalized additive models. 4.4 Examples of the application of GAMs. 4.5 Summary. 5. Classification and Regression Trees. 5.1 Introduction. 5.2 Tree-based models. 5.3 Birthweight of babies. 5.4 Summary. 6. Survival Analysis I: Cox's Regression. 6.1 Introduction. 6.2 The survivor function. 6.3 The hazard function. 6.4 Cox's proportional hazards model. 6.5 Left truncation. 6.6 Extending Cox's model by stratification. 6.7 Checking the specification of a Cox model. 6.8 Summary. 7. Survival Analysis II: Time-dependent Covariates, Frailty and Tree Models. 7.1 Introduction. 7.2 Time-dependent covariates. 7.3 Random effects models for survival data. 7.4 Tree-structured survival analysis. 7.5 Summary. 8. Bayesian Methods and Meta-analysis. 8.1 Introduction. 8.2 Bayesian methods. 8.3 Meta-analysis. 8.4 Summary. 9. Exact Inference for Categorical Data. 9.1 Introduction. 9.2 Small expected values in contingency table, Yates' correction and Fisher's exact test. 9.3 Examples of the use of exact p-values. 9.4 Logistic regression and conditional logistic regression for sparse data. 9.5 Summary. 10. Finite Mixture Models. 10.1 Introduction. 10.2 Finite mixture distributions. 10.3 Estimating the parameters in finite mixture models. 10.4 Some examples of the application of finite mixture densities in medical research. 10.5 Latent class analysis – mixtures for binary data. 10.6 Summary. Glossary. Appendix A: Statistical Graphics in Medical Invetigations. A.1 Introduction. A.2 Probability plots. A.3 Scatterplots and beyond. A.4 Scatterplot matrices. A.5 Coplots and trellis graphics. Appendix B: Answers to Selected Exercises. References. Index.

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Book SynopsisBatch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments. A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data. An extensive overview of current standardization initiatives. All datasets aTable of ContentsList of Contributors xiii Foreword xvii Preface xix 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction 1Andreas Scherer 2 Microarray Platforms and Aspects of Experimental Variation 5John A Coller Jr 2.1 Introduction 5 2.2 Microarray Platforms 6 2.2.1 Affymetrix 6 2.2.2 Agilent 7 2.2.3 Illumina 7 2.2.4 Nimblegen 8 2.2.5 Spotted Microarrays 8 2.3 Experimental Considerations 9 2.3.1 Experimental Design 9 2.3.2 Sample and RNA Extraction 9 2.3.3 Amplification 12 2.3.4 Labeling 13 2.3.5 Hybridization 13 2.3.6 Washing 14 2.3.7 Scanning 15 2.3.8 Image Analysis and Data Extraction 16 2.3.9 Clinical Diagnosis 17 2.3.10 Interpretation of the Data 17 2.4 Conclusions 17 3 Experimental Design 19Peter Grass 3.1 Introduction 19 3.2 Principles of Experimental Design 20 3.2.1 Definitions 20 3.2.2 Technical Variation 21 3.2.3 Biological Variation 21 3.2.4 Systematic Variation 22 3.2.5 Population, Random Sample, Experimental and Observational Units 22 3.2.6 Experimental Factors 22 3.2.7 Statistical Errors 23 3.3 Measures to Increase Precision and Accuracy 24 3.3.1 Randomization 25 3.3.2 Blocking 25 3.3.3 Replication 25 3.3.4 Further Measures to Optimize Study Design 26 3.4 Systematic Errors in Microarray Studies 28 3.4.1 Selection Bias 28 3.4.2 Observational Bias 28 3.4.3 Bias at Specimen/Tissue Collection 29 3.4.4 Bias at mRNA Extraction and Hybridization 30 3.5 Conclusion 30 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies 33Naomi Altman 4.1 Introduction 33 4.1.1 Batch Effects 35 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 35 4.2.1 Using the Linear Model for Design 37 4.2.2 Examples of Design Guided by the Linear Model 37 4.3 Blocks and Batches 39 4.3.1 Complete Block Designs 39 4.3.2 Incomplete Block Designs 39 4.3.3 Multiple Batch Effects 40 4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 41 4.4.1 Between and Within Batch Normalization with Multi-array Methods 43 4.4.2 Statistical Adjustment 46 4.5 Sample Pooling and Sample Splitting 47 4.5.1 Sample Pooling 47 4.5.2 Sample Splitting: Technical Replicates 48 4.6 Pilot Experiments 49 4.7 Conclusions 49 Acknowledgements 50 5 Aspects of Technical Bias 51Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer 5.1 Introduction 51 5.2 Observational Studies 52 5.2.1 Same Protocol, Different Times of Processing 52 5.2.2 Same Protocol, Different Sites (Study 1) 53 5.2.3 Same Protocol, Different Sites (Study 2) 55 5.2.4 Batch Effect Characteristics at the Probe Level 57 5.3 Conclusion 60 6 Bioinformatic Strategies for cDNA-Microarray Data Processing 61Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten, and Patrik Rydén 6.1 Introduction 61 6.1.1 Spike-in Experiments 62 6.1.2 Key Measures – Sensitivity and Bias 63 6.1.3 The IC Curve and MA Plot 63 6.2 Pre-processing 64 6.2.1 Scanning Procedures 65 6.2.2 Background Correction 65 6.2.3 Saturation 67 6.2.4 Normalization 68 6.2.5 Filtering 70 6.3 Downstream Analysis 71 6.3.1 Gene Selection 71 6.3.2 Cluster Analysis 71 6.4 Conclusion 73 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance 75Nysia I George and James J Chen 7.1 Introduction 75 7.1.1 Microarray Gene Expression Data 76 7.1.2 Analysis of Variance in Gene Expression Data 77 7.2 Variance Component Analysis across Microarray Platforms 78 7.3 Methodology 78 7.3.1 Data Description 78 7.3.2 Normalization 79 7.3.3 Gene-Specific ANOVA Model 81 7.4 Application: The MAQC Project 81 7.5 Discussion and Conclusion 85 Acknowledgements 85 8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set 87Michael J Boedigheimer, Jeff W Chou, J Christopher Corton, Jennifer Fostel, Raegan O’Lone, P Scott Pine, John Quackenbush, Karol L Thompson, and Russell D Wolfinger 8.1 Introduction 87 8.2 Methodology 89 8.3 Results 89 8.3.1 Assessment of Smooth Bias in Baseline Expression Data Sets 89 8.3.2 Relationship between Smooth Bias and Signal Detection 91 8.3.3 Effect of Smooth Bias Correction on Principal Components Analysis 92 8.3.4 Effect of Smooth Bias Correction on Estimates of Attributable Variability 94 8.3.5 Effect of Smooth Bias Correction on Detection of Genes Differentially Expressed by Fasting 95 8.3.6 Effect of Smooth Bias Correction on the Detection of Strain-Selective Gene Expression 96 8.4 Discussion 97 Acknowledgements 99 9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions 101Walter Liggett, Jean Lozach, Anne Bergstrom Lucas, Ron L Peterson, Marc L Salit, Danielle Thierry-Mieg, Jean Thierry-Mieg, and Russell D Wolfinger 9.1 Introduction 101 9.2 Input Mass Effect on the Amount of Normalization Applied 103 9.3 Probe-by-Probe Modeling of the Input Mass Effect 103 9.4 Further Evidence of Batch Effects 108 9.5 Conclusions 110 10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods 113W Evan Johnson and Cheng li 10.1 Introduction 113 10.1.1 Bayesian and Empirical Bayes Applications in Microarrays 114 10.2 Existing Methods for Adjusting Batch Effect 115 10.2.1 Microarray Data Normalization 115 10.2.2 Batch Effect Adjustment Methods for Large Sample Size 115 10.2.3 Model-Based Location and Scale Adjustments 116 10.3 Empirical Bayes Method for Adjusting Batch Effect 117 10.3.1 Parametric Shrinkage Adjustment 117 10.3.2 Empirical Bayes Batch Effect Parameter Estimates using Nonparametric Empirical Priors 120 10.4 Data Examples, Results and Robustness of the Empirical Bayes Method 121 10.4.1 Microarray Data with Batch Effects 121 10.4.2 Results for Data Set 1 124 10.4.3 Results for Data Set 2 124 10.4.4 Robustness of the Empirical Bayes Method 126 10.4.5 Software Implementation 127 10.5 Discussion 128 11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis 131Wynn L Walker and Frank R Sharp 11.1 Introduction 131 11.2 Methodology 133 11.2.1 Data Description 133 11.2.2 Empirical Bayes Method for Batch Adjustment 134 11.2.3 Naïve t-test Batch Adjustment 135 11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients 135 11.3.1 Removal of Cross-Experimental Batch Effects 135 11.3.2 Removal of Within-Experimental Batch Effects 136 11.3.3 Removal of Batch Effects: Empirical Bayes Method versus t-Test Filter 137 11.4 Discussion and Conclusion 138 11.4.1 Methods for Batch Adjustment Within and Across Experiments 138 11.4.2 Bayesian Approach is Well Suited for Modeling Cross-Experimental Batch Effects 139 11.4.3 Implications of Cross-Experimental Batch Corrections for Clinical Studies 139 12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data 141Jianying Li, Pierre R Bushel, Tzu-Ming Chu, and Russell D Wolfinger 12.1 Introduction 141 12.2 Methods 143 12.2.1 Principal Components Analysis 143 12.2.2 Variance Components Analysis and Mixed Models 145 12.2.3 Principal Variance Components Analysis 145 12.3 Experimental Data 146 12.3.1 A Transcription Inhibition Study 146 12.3.2 A Lung Cancer Toxicity Study 147 12.3.3 A Hepato-toxicant Toxicity Study 147 12.4 Application of the PVCA Procedure to the Three Example Data Sets 148 12.4.1 PVCA Provides Detailed Estimates of Batch Effects 148 12.4.2 Visualizing the Sources of Batch Effects 149 12.4.3 Selecting the Principal Components in the Modeling 150 12.5 Discussion 153 13 Batch Profile Estimation, Correction, and Scoring 155Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger 13.1 Introduction 155 13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects 157 13.2.1 Batch Profile Estimation 159 13.2.2 Batch Profile Correction 160 13.2.3 Batch Profile Scoring 161 13.2.4 Cross-Validation Results 162 13.3 Discussion 164 Acknowledgements 165 14 Visualization of Cross-Platform Microarray Normalization 167Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J S Marron 14.1 Introduction 167 14.2 Analysis of the NCI 60 Data 169 14.3 Improved Statistical Power 174 14.4 Gene-by-Gene versus Multivariate Views 178 14.5 Conclusion 181 15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis 183Lev Klebanov and Andreas Scherer 15.1 Introduction 183 15.2 Aggregated Expression Intensities 185 15.3 Covariance between Log-Expressions 186 15.4 Conclusion 189 Acknowledgements 190 16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies 191Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick, and Weida Tong 16.1 Introduction 191 16.2 Potential Sources of Spurious Associations 192 16.2.1 Spurious Associations Related to Study Design 194 16.2.2 Spurious Associations Caused in Genotyping Experiments 195 16.2.3 Spurious Associations Caused by Genotype Calling Errors 195 16.3 Batch Effects 196 16.3.1 Batch Effect in Genotyping Experiment 196 16.3.2 Batch Effect in Genotype Calling 197 16.4 Conclusion 201 Disclaimer 201 17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development 203Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng 17.1 Introduction 203 17.2 Theoretical Framework 204 17.3 Systems-Biological Concepts in Medicine 204 17.4 General Conceptual Challenges 205 17.5 Strategies for Gene Expression Biomarker Development 205 17.5.1 Phase 1: Clinical Phenotype Consensus Definition 206 17.5.2 Phase 2: Gene Discovery 207 17.5.3 Phase 3: Internal Differential Gene List Confirmation 209 17.5.4 Phase 4: Diagnostic Classifier Development 209 17.5.5 Phase 5: External Clinical Validation 210 17.5.6 Phase 6: Clinical Implementation 211 17.5.7 Phase 7: Post-Clinical Implementation Studies 212 17.6 Conclusions 213 18 Data, Analysis, and Standardization 215Gabriella Rustici, Andreas Scherer, and John Quackenbush 18.1 Introduction 215 18.2 Reporting Standards 216 18.3 Computational Standards: From Microarray to Omic Sciences 219 18.3.1 The Microarray Gene Expression Data Society 219 18.3.2 The Proteomics Standards Initiative 220 18.3.3 The Metabolomics Standards Initiative 220 18.3.4 The Genomic Standards Consortium 220 18.3.5 Systems Biology Initiatives 221 18.3.6 Data Standards in Biopharmaceutical and Clinical Research 221 18.3.7 Standards Integration Initiatives 222 18.3.8 The MIBBI project 223 18.3.9 OBO Foundry 223 18.3.10 FuGE and ISA-TAB 223 18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods 226 18.5 Conclusions and Future Perspective 228 References 231 Index 245

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Book SynopsisPraise for the Second Edition: ...a grand feast for biostatisticians. It stands ready to satisfy the appetite of any pharmaceutical scientist with a respectable statistical appetite. Journal of Clinical Research Best Practices The Third Edition of Design and Analysis of Clinical Trials provides complete, comprehensive, and expanded coverage of recent health treatments and interventions. Featuring a unified presentation, the book provides a well-balanced summary of current regulatory requirements and recently developed statistical methods as well as an overview of the various designs and analyses that are utilized at different stages of clinical research and development. Additional features of this Third Edition include: New chapters on biomarker development and target clinical trials, adaptive design, trials for evaluating diagnostic devices, statistical methods for translational medicine, and traditional Chinese medicine Trade Review�In summary, this third edition is an impressive expansion beyond a remarkable second edition. This book would be good reference for biostatisticians, clinical researchers, and pharmaceutical scientists in clinical research and development.� (Journal of Biopharmaceutical Statistics, 1 July 2014)"Design and Analysis of Clinical Trials: Concepts and Methodologies, Third Edition is a grand feast for biostatisticians. It stands ready to satisfy the appetite of any pharmaceutical scientist with a respectable statistical appetite...Essential reading for clinical research professionals." (Journal of Clinical Research Best Practice February 2014)Table of ContentsPreface xi PART I PRELIMINARIES 1 Introduction 3 1.1 What are Clinical Trials?, 3 1.2 History of Clinical Trials, 4 1.3 Regulatory Process and Requirements, 10 1.4 Investigational New Drug Application, 17 1.5 New Drug Application, 24 1.6 Clinical Development and Practice, 31 1.7 AIMS and Structure of the Book, 42 2 Basic Statistical Concepts 45 2.1 Introduction, 45 2.2 Uncertainty and Probability, 46 2.3 Bias and Variability, 49 2.4 Confounding and Interaction, 57 2.5 Descriptive and Inferential Statistics, 66 2.6 Hypotheses Testing and p-Values, 68 2.7 Clinical Significance and Clinical Equivalence, 75 2.8 Reproducibility and Generalizability, 79 3 Basic Design Considerations 85 3.1 Introduction, 85 3.2 Goals of Clinical Trials, 86 3.3 Target Population and Patient Selection, 90 3.4 Selection of Controls, 97 3.5 Statistical Considerations, 105 3.6 Other Issues, 112 3.7 Discussion, 115 4 Randomization and Blinding 117 4.1 Introduction, 117 4.2 Randomization Models, 118 4.3 Randomization Methods, 124 4.4 Implementation of Randomization, 144 4.5 Generalization of Controlled Randomized Trials, 149 4.6 Blinding, 153 4.7 Discussion, 160 PART II DESIGNS AND THEIR CLASSIFICATIONS 5 Designs for Clinical Trials 165 5.1 Introduction, 165 5.2 Parallel Group Designs, 167 5.3 Clustered Randomized Designs, 172 5.4 Crossover Designs, 177 5.5 Titration Designs, 185 5.6 Enrichment Designs, 191 5.7 Group Sequential Designs, 195 5.8 Placebo-Challenging Designs, 197 5.9 Blinded Reader Designs, 203 5.10 Discussion, 207 6 Designs for Cancer Clinical Trials 211 6.1 Introduction, 211 6.2 General Considerations for Phase I Cancer Clinical Trials, 213 6.3 Single-Stage Up-and-Down Phase I Designs, 214 6.4 Two-Stage Up-and-Down Phase I Designs, 217 6.5 Continual Reassessment Method Phase I Designs, 219 6.6 Optimal and Flexible Multiple-Stage Designs, 222 6.7 Randomized Phase II Designs, 229 6.8 Discussion, 232 7 Classification of Clinical Trials 237 7.1 Introduction, 237 7.2 Multicenter Trials, 238 7.3 Superiority Trials, 245 7.4 Active Control and Equivalence/Noninferiority Trials, 248 7.5 Dose–Response Trials, 261 7.6 Combination Trials, 266 7.7 Bridging Studies and Global Trials, 278 7.8 Vaccine Clinical Trials, 285 7.9 QT Studies, 291 7.10 Discussion, 299 PART III ANALYSIS OF CLINICAL DATA 8 Analysis of Continuous Data 305 8.1 Introduction, 305 8.2 Estimation, 306 8.3 Test Statistics, 310 8.4 Analysis of Variance, 316 8.5 Analysis of Covariance, 323 8.6 Nonparametric Methods, 325 8.7 Repeated Measures, 332 8.8 Discussion, 341 9 Analysis of Categorical Data 343 9.1 Introduction, 343 9.2 Statistical Inference for One Sample, 345 9.3 Inference of Independent Samples, 358 9.4 Ordered Categorical Data, 364 9.5 Combining Categorical Data, 368 9.6 Model-Based Methods, 374 9.7 Repeated Categorical Data, 382 9.8 Discussion, 387 10 Censored Data and Interim Analysis 389 10.1 Introduction, 389 10.2 Estimation of the Survival Function, 391 10.3 Comparison Between Survival Functions, 399 10.4 Cox’s Proportional Hazard Model, 405 10.5 Calendar Time and Information Time, 419 10.6 Group Sequential Methods, 424 10.7 Discussion, 438 11 Sample Size Determination 441 11.1 Introduction, 441 11.2 Basic Concept, 442 11.3 Two Samples, 447 11.4 Multiple Samples, 456 11.5 Censored Data, 459 11.6 Dose–Response Studies, 464 11.7 Crossover Designs, 471 11.8 Equivalence and Noninferiority Trials, 481 11.9 Multiple-Stage Design in Cancer Trials, 490 11.10 Multinational Trials, 490 11.11 Comparing Variabilities, 500 11.12 Discussion, 517 PART IV ISSUES IN EVALUATION 12 Issues in Efficacy Evaluation 521 12.1 Introduction, 521 12.2 Baseline Comparison, 523 12.3 Intention-to-Treat Principle and Efficacy Analysis, 528 12.4 Adjustment for Covariates, 536 12.5 Multicenter Trials, 541 12.6 Multiplicity, 548 12.7 Data Monitoring, 558 12.8 Use of Genetic Information for Evaluation of Efficacy, 564 12.9 Sample Size Reestimation, 570 12.10 Discussion, 572 13 Safety Assessment 573 13.1 Introduction, 573 13.2 Extent of Exposure, 574 13.3 Coding of Adverse Events, 582 13.4 Analysis of Adverse Events, 595 13.5 Analysis of Laboratory Data, 602 13.6 Analysis of QT/QTc Prolongation, 610 13.7 Discussion, 615 PART V RECENT DEVELOPMENT 14 Biomarkers and Targeted Clinical Trials 619 14.1 Introduction, 619 14.2 Concepts and Strategies, 620 14.3 Biomarker Development and Validation, 623 14.4 Designs of Targeted Clinical Trials, 630 14.5 Analyses of Targeted Clinical Trials, 640 14.6 Discussion, 647 15 Trials for Evaluating Accuracy of Diagnostic Devices 649 15.1 Introduction, 649 15.2 Study Design, 651 15.3 Measures of Diagnostic Accuracy, 656 15.4 Reporting Results, 663 15.5 Sample Size Estimation, 672 15.6 Discussion, 675 16 Statistical Methods in Translational Medicine 677 16.1 Introduction, 677 16.2 Biomarker Development, 678 16.3 Bench-to-Bedside, 682 16.4 Animal Model Versus Human Model, 689 16.5 Translation in Study Endpoints, 691 16.6 Bridging Studies, 696 16.7 Discussion, 699 16.8 Appendix, 700 17 Adaptive Clinical Trial Designs 703 17.1 Introduction, 703 17.2 What Is Adaptive Design?, 704 17.3 Well-Understood and Less Well-Understood Designs, 709 17.4 Clinical/Statistical and Regulatory Perspectives, 713 17.5 Impact of Protocol Amendments, 716 17.6 Challenges in By-Design Adaptations, 721 17.7 Obstacles of Retrospective Adaptations, 727 17.8 Discussion, 729 18 Traditional Chinese Medicine 733 18.1 Introduction, 733 18.2 Fundamental Differences, 734 18.3 Basic Considerations of TCM Clinical Trials, 741 18.4 Other Issues in TCM Research and Development, 744 18.5 Consortium for Globalization of Traditional Chinese Medicine, 751 18.6 Discussion, 752 PART VI CONDUCT OF CLINICAL TRIALS 19 Preparation and Implementation of a Clinical Protocol 755 19.1 Introduction, 755 19.2 Structure and Components of a Protocol, 756 19.3 Points to be Considered and Common Pitfalls During Development and Preparation of a Protocol, 762 19.4 Common Departures for Implementation of a Protocol, 765 19.5 Monitoring, Audit, and Inspection, 771 19.6 Quality Assessment of a Clinical Trial, 775 19.7 Discussion, 777 20 Data Management of a Clinical Trial 779 20.1 Introduction, 779 20.2 Regulatory Requirements, 781 20.3 Development of Case Report Forms, 783 20.4 Database Development, 787 20.5 Data Entry, Query, and Correction, 788 20.6 Data Validation and Quality, 791 20.7 Database Lock, Archive, and Transfer, 792 20.8 Critical Issues, 795 References 799 Appendix A 845 Index 851

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Book Synopsis A simple and systematic guide to the planning and performance of investigations concerned with health and disease and with health care Offers researchers help in choosing a topic and to think about shaping objectives and ideas and to link these with the appropriate choice of method Fully updated with new sections on the use of the Web and computer programmes freely available in the planning, performance or analysis of studies Table of ContentsPreface vii 1. First steps 1 2. Types of investigation 13 3. Stages of an investigation 35 4. Formulating the objectives 39 5. The objectives of evaluative studies 49 6. The study population 61 7. Control groups 69 8. Sampling 77 9. Selecting cases and controls for case-control studies 91 10. The variables 101 11. Defining the variables 109 12. Definitions of diseases 117 13. Scales of measurement 125 14. Composite scales 133 15. Methods of collecting data 143 16. Reliability 151 17. Validity 161 18. Interviews and self-administered questionnaires 179 19. Constructing a questionnaire 193 20. Surveying the opinions of a panel: consensus methods 203 21. The use of documentary sources 209 22. Planning the records 225 23. Planning the handling of data 233 24. Pretests and other preparations 241 25. Collecting the data 247 26. Statistical analysis 251 27. Interpreting the findings 259 28. Making sense of associations 269 29. Application of the study findings 297 30. Writing a report 305 31. Rapid epidemiological methods 313 32. Clinical trials 325 33. Programme trials 345 34. Community-oriented primary care 357 35. Using the Web for health research 373 Appendix A Community appraisal: a checklist 383 Appendix B Random numbers 387 Appendix C Free computer programs 389 Index 407

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Book SynopsisA respected introduction to biostatistics, thoroughly updated and revised The first edition of Biostatistics: A Methodology for the Health Sciences has served professionals and students alike as a leading resource for learning how to apply statistical methods to the biomedical sciences. This substantially revised Second Edition brings the book into the twenty-first century for today's aspiring and practicing medical scientist. This versatile reference provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Written with an eye toward the use of computer applications, the book examines the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference; explores more advanced statistical methods; and illustrates important current uses of biostatistics. New to this edition are discussions of LongitTrade Review"…an excellent introductory biostatics book with several examples and easy to understand interpretations of data analysis results." (Journal of Statistical Computation and Simulation, September 2005) "The book would serve as a good springboard for structuring an introductory biostatistics course…" (The American Statistician, August 2005) "...the book will be a great resource for new readers as well as professionals in the field of health research." (E-STREAMS, May 2005) "...this comprehensive book should continue to merit consideration by anyone looking for a desktop reference in biostatistics." (Technometrics, May 2005) “…this second edition adds two new chapters: one on randomised clinical trials, and another on longitudinal data analysis.” (Journal of Applied Statistics, Vol.32, No.3, April 2005) "Beyond comprehensiveness, the differentiating characteristics of this textbook is the use of medical examples...a text that can be fully appreciated by those seeking a technical statistical reference…" (The Annals of Pharmacotherapy, March 2005) “…enjoyable reading…approachable and comprehensible…a useful addition to the bookshelf of any biomedical researcher…” (Journal of the Royal Statistical Society, Series A, Vol.168, No.2, March 2005) “…this updated edition will ensure the ongoing usefulness of this valuable resource…” (Short Book Reviews, Vol.24, No.3, December 2004) "…chock full of information and careful examination…scientists and researchers who access this information will gain much new insight…" (Electric Review, October/ November 2004) "The authors have dealt with the subject matter in truly comprehensive terms… scientists and researchers who access this information will gain much new insight..." (Electric Review, July/August 2004) Table of ContentsPreface to the First Edition. Preface to the Second Edition. 1. Introduction to Biostatistics. 2. Biostatistical Design of Medical Studies. 3. Descriptive Statistics. 4. Statistical Inference: Populations and Samples. 5. One- and Two-Sample Inference. 6. Counting Data. 7. Categorical Data: Contingency Tables. 8. Nonparametric, Distribution-Free and Permutation Models: Robust Procedures. 9. Association and Prediction: Linear Models with One Predictor Variable. 10. Analysis of Variance. 11. Association and Prediction: Multiple Regression Analysis, Linear Models with Multiple Predictor Variables. 12. Multiple Comparisons. 13. Discrimination and Classification. 14. Principal Component Analysis and Factor Analysis. 15. Rates and Proportions. 16. Analysis of the Time to an Event: Survival Analysis. 17. Sample Sizes for Observational Studies. 18. Longitudinal Data Analysis. 19. Randomized Clinical Trials. 20. A Personal Postscript. Appendix. Author Index. Subject Index. Symbol Index.

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Book SynopsisIntroduces applied research areas and a number of real-life questions and examples with basic methods in nonparametric statistics, including the concept of censoring, which distinguishes survival analysis from other areas of statistics.Table of ContentsBasic Concepts in Survival Analysis. Estimation of Functions and Parameters. Comparison of Survival Distributions. Correlation and Regression Analyses. Appendices. References. Index.

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Book SynopsisTHE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATANOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling tTrade Review“This is a great book for anyone analyzing time-to-event data. Researchers interested in the underlying theory will have to go elsewhere..” (Stat Papers, 1 December 2012) "It is well suited for teaching a graduate-level course in medical statistics, and the data sets used in the book are available online." (Biometrical Journal, August 2009) "This is a superb resource - a practical guide with up-to-date applications. The authors are excellent teachers of the mathematics and application of survival data regression modeling." (Doodys, August 2009) "The extensive and detailed coverage of the process of survival model fitting, as well as the applied exercises, make this textbook an excellent choice for an applied survival analysis course." (Journal of Biopharmaceutical Statistics, Volume 18, Issue 6, 2008)Table of ContentsPreface xi 1. Introduction to Regression Modeling of Survival Data 1 2. Descriptive Methods for Survival Data 16 3. Regression Models for Survival Data 67 4. Interpretation of a Fitted Proportional Hazards Regression Model 92 5. Model Development 132 6. Assessment of Model Adequacy 169 7. Extensions of the Proportional Hazards Model 207 8. Parametric Regression Models 244 9. Other Models and Topics 286 Appendix 1: The Delta Method 355 Appendix 2: An Introduction to the Counting Process Approach to Survival Analysis 359 Appendix 3: Percentiles for Computation of the Hall and Wellner Confidence Band 364 References 365 Index 383

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Book SynopsisDevoted to explaining the terms used specifically in pharmacoepidemiology, this text is the first English language dictionary in this area. The entries have been reviewed and revised by experts around the world.Trade Review"Good reference source; likely to enjoy a long life. It's a buy." (Pharmaceutical Physician, June 2001) "Begaud has assembled this dictionary which contains clear definitions for terms often thrown around with little understanding" (British Journal of Clinical Pharmacology, July 2001) "...translated and adapted from the third edition of Dictionnair de Pharmacoepidemiologie.... It has been substantially revised to take into account comments from professionals using the previous editions." (SciTech Book News, Vol. 25, No. 4, December 2001)

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Book SynopsisThe aim of this book is to suggest and exemplify a systematic methodology for analysing survival data which contains immune, or cured individuals, denoted generically as long-term survivors. Such data occurs in medical and epidemiological applications, where the intention may be to identify whether or not cured or immune individuals are present in a population, perhaps as a result of treatments given; in the analysis of recidivism data in criminology, where the intentions are similar with respect to prisoners released from and possibly returning to prison; and in many other areas where followup data is available on individuals, with the possibility that not all suffer the event under investigation. Both nonparametric and parametric methods are proposed and developed. The effects of covariate information can be assessed via a kind of generalised linear framework in the parametric analyses. The proposed methodologies are supported by asymptotic analyses and simulations of real situationsTrade Review"The book contains an admirable blend of theory and practice beingclearly explained and illustrated with realistic examples ofsurvival analyses from medical and criminological studies." "...an introduction to the analysis of survival data..." (Quarterlyof Applied Mathematics, Vol. LVIII, No. 4,December 2000)Table of ContentsFormulating Tests for the Presence of Immunes and SufficientFollow-up. Properties of the Kaplan-Meier Estimator. Nonparametric Estimation and Testing. Parametric Models for Single Samples. The Use of Concomitant Information. Large Sample Properties of Parametric Models: Single Samples. Large-Sample Properties of Parametric Models with Covariates. Further Topics. References. Statistical Tables. Index.

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Book SynopsisThe demand for safely purified water and the concerns relating to illness and disease from water sources are of prime importance to all countries. This book explores waterborne diseases and reflects the increasing environmental awareness and understanding of public health matters.Trade Review"It is well written, clearly laid out and easily understood. If you come across a water-related disease problem, this should be the first book to turn to." (Environmental Health Journal, March 1999)Table of ContentsAcknowledgments vii Introduction viii 1 An Introduction to the Science and Art of Epidemiology 1 2 Water Supply and Distribution 17 3 Drinking Water and Waterborne Disease 27 4 Illness Associated with Recreational Contact with Water 42 5 Dracunculiasis (Guinea Worm Infestion) 52 6 Schistosomiasis 57 7 Giardiasis 68 8 Cryptosporidiosis 80 9 Cyclospora 91 10 Naegleria 93 11 Cyanobacteria 95 12 Cholera and Other Vibrios 103 13 Typhoid and Paratyphoid Fevers and Other Salmonella Infections 116 14 Shigellosis (Bacillary Dysentery) 124 15 Campylobacteriosis 133 16 Escherichia coli 143 17 Yersinia Infections 151 18 Plesiomonas Infections 157 19 Aeromonas Infections 160 20 Pseudomonas Infections 165 21 Melioidosis 172 22 Legionnaire’s Disease 175 23 Leptospirosis 182 24 Mycobacterial Disease 189 25 Tularaemia 199 26 Heliocobacter Infections 202 27 Viral Hepatitis 206 28 Viral Gastroenteritis 222 29 Enterovirus Infections Including Poliomyelitis 232 30 Adenoviral Infections 240 31 Chemical Poisoning and Drinking Water 245 32 Cancer and Water 274 33 Adverse Pregnancy Outcomes and Water 293 References 301 Index 365

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Book SynopsisIn sequential clinical trials, data from patients treated at the beginning of a trial can be analyzed while the trial is still in progress and subsequent patients can be recruited. This analysis provides stopping rules whereby the number of patients involved can be limited without affecting the results.Trade Review"...This book is clear, it provides many details and is well written..." (Statistical Methods in Medical Research, No.11, 2002)Table of ContentsClinical Trials. Allocating Patients to Treatments. Measurement of Treatment Difference. The Design of a Sequential Trial Using the Boundaries Approach. The Analysis of a Sequential Trial. Alternative Approaches to the Design and Analysis of Sequential Clinical Trials. Prognostic Factors. The Comparison of More than Two Treatments. Implementation of Sequential Methods: Some Examples. References. Appendix. Index.

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Book SynopsisMeta--analysis is one of the main statistical methods used in clinical trials. Previous accounts of meta--analysis have given the impression that the topic is a series of separate techniques. This book provides a unified approach, developing the subject from mathematical theory through to practical discussions of implementation.Trade Review"…highly recommended as essential reading for medical statisticians…" (Short Book Reviews, April 2003) "...comprehensive and well illustrated contribution to the subject...useful for graduate students of medical statistics..." (Statistics in Medicine, Vol 23(3), 15 February 2004)Table of ContentsIntroduction Protocol development Estimating the treatment difference in an individual trial Combining estimates of a treatment difference across trials Meta-analysis using individual patient data Dealing with heterogeneity Presentation and interpretation of results Selection bias Dealing with non-standard datasets Inclusion of trials with different study designs A Bayesian approach to meta-analysis Sequential methods for meta-analysis Appendix Methods of estimation and hypothesis testing

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Book SynopsisMultilevel modelling facilitates the analysis of hierarchical data where observations may be nested within higher levels of classification. In health care research, for example, a study may be undertaken to determine the variability of patient outcomes where these also vary by hospital or health care region.Trade Review"...contains 13 well written chapters by experts...the references are recent and useful. It can be used as a textbook in graduate level modeling course." (Journal of Statistical Computation & Simulation, May 2004) "...exhibits a marvellous degree of coherence and clarity..." (Pharmaceutical Statistics, Vol 2, 2003) "...good introductions to multilevel models, and plenty of examples..." (Zentralblatt Math, 2003) "...I believe that the book all in all fulfils this promise..." (Statistics in Medicine, No.21, 2002) "...a very readable book whose audience does not seem to be limited to statisticians." (Technometrics, Vol. 44, No. 4, November 2002) "Highly recommended to biostatisticians, health care professionals and public health researchers in the application of multilevel model. It can also be used as a reference book for postgraduate students studying medical statistics." (ISCB News, December 2001)Table of ContentsPreface. Contributors. Introduction. Multilevel Data and Their Analysis (M. Healy). Modelling Repeated Measurements (H. Glodstein and G. Woodhouse). Binomial Regression (N. Rice). Poisson Regression (I. Langford and R. Day). Multivariate Multilevel Models (A. McLeod). Outliers, Robustness and the Detection of Discrepant Data (T. Lewis and I. Langford). Modelling Non-Hierarchical Structures (J. Rasbash and W. Browne). Multinomial Regression (M. Yang). Institutional Performance (E. Marshall and D. Spiegelhalter). Spatial Analysis (A. Leyland). Sampling (T. Snijders). Further Topics in Multilevel Modelling (H. Goldstein and A. Leyland). Software for Multilevel Analysis (J. de Leeuw and I. Kreft). References. Index.

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Book SynopsisIn the only history of its kind, Etheridge traces the development of the Centers for Disease Control from its inception as a malaria control unit during World War II through the mid-1980s . The eradication of smallpox, the struggle to identify an effective polio vaccine, the unraveling of the secrets of Legionnaires' disease, and the shock over the identification of the HIV virus are all chronicled here. Drawing on hundreds of interviews and source documents, Etheridge vividly recreates the vital decision-making incidents that shaped both the growth of this institution as well as the state of public health in this country for the last five decades. We follow the development of the institution as it was transformed by the will and the imagination of remarkable individuals such as Dr. Joseph Mountin, one of the first heads of the CDC. Often characterized as abrasive and impatient, Mountin pushed the CDC to become a vital player in eradicating the threat of communicable disease in the United States. Others such as Dr. Alexander Langmuir brought the expertise necessary to establish epidemiology as one of the primary functions of the CDC. Created to serve the states and to answer any call for help whether routine or extraordinary, the CDC is now widely recognized as one of the world's premier public health institutions.Table of ContentsList of Charts List of Abbreviations Preface 1. War and the Mosquito 2. The Lengthened Shadow of a Man 3. The Disease Detectives 4. Building the Temple 5. Establishing Credibility 6. Building the Temple 7. 1600 Clifton Road 8. A Call to Arms 9. The Candidate for Surgeon General 10. Immunization: The First Crusade 11. The Lengthened Shadow of a Man 12. Immunization: The Second Crusade 13. Over Oceans and into Space 14. The Crusade against Smallpox 15. Africa's New Challenges 16. Acquisitions 17. In Pursuit of the Promised Land 18. 1976 19. Aftermath 20. The Lengthened Shadow of a Man 21. Immunization: The Third Crusade 22. Maintaining Credibility 23. Toward the Twenty-first Century 24. Discovery of the AIDS Epidemic Epilogue Notes A Bibliographical Note Index

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Book SynopsisEvery day we are bombarded by television ads, public service announcements, and media reports warning of dire risks to our health and offering solutions to help us lower those risks. This book intends to help consumers sort through this daily barrage by teaching them how to interpret the numbers behind the messages.Trade Review"Short and simple... Present(s) the basic skills necessary in navigating today's confusing health-media landscape. " Library Journal " Delightful and educational reading, simple enough for laypeople to understand yet academic enough to meet the needs of ... students." -- L. Synovitz Choice "Know Your Chances is an accessible and empowering text." Journal Of Biosocial Science "A great reminder that ... medical claims should always be evaluated by how they affect you and your current state of health." Tampa Tribune "A great reminder that ... medical claims should always be evaluated by how they affect you and your current state of health." Highlands Today "Read this book first and then think again." Time MagazineTable of ContentsWhat This Book Is About PART ONE. WHAT IS MY RISK? 1. Understanding Risk 2. Putting Risk in Perspective 3. Risk Charts: A Way to Get Perspective PART TWO. CAN I REDUCE MY RISK? 4. Judging the Benefit of a Health Intervention 5. Not All Benefits Are Equal: Understand the Outcome PART THREE. DOES RISK REDUCTION HAVE DOWNSIDES? 6. Consider the Downsides 7. Do the Benefits Outweigh the Downsides? PART FOUR. DEVELOPING A HEALTHY SKEPTICISM 8. Beware of Exaggerated Importance 9. Beware of Exaggerated Certainty 10. Who's Behind the Numbers? EXTRA HELP Quick Summary Glossary Number Converter Risk Charts Credible Sources of Health Statistics Notes Index

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Book SynopsisTells how smallpox, a disease that killed, blinded, and scarred millions over centuries of human history, was completely eradicated in a spectacular triumph of medicine and public health.Trade Review"Dr. Foege's book ... remind[s] us how fragile life looks." New York Times "Bounces the reader along with him in his jeep, on motorbikes over rugged terrain and on bustling trains... (And) shows what can be accomplished when governments and thousands of health workers focus on a single objective. " Wall Street Journal "[Foege] writes a mixture of memoir, dry public health guide and riveting tale of an all-consuming mission." -- Tiffany O'Callaghan New Scientist "A readable and thorough account by a key player in this outstanding victory for public health." Library Journal "A reminder of the importance of preventive medicine." Jama "A great, quick, and intensely personal read about the inside story of Foege's revolutionary idea and powerful actions... Foege was wise before his time." Medpage Today "Demonstrate[s] the enormous benefit that can accrue to mankind when a determined and ambitious band of individuals come together." The Lancet "Gives an intimate sense of what it is like to work on the ground in some of the world's most impoverished countries -- and tells what it is like to contribute to programs that really do change the world." Scienceblogs.com/The Guardian "Inspiring... A fascinating human interest account that is expertly merged with scientific facts." -- Pascal James Imperato Jrnl Of Community Health "A fascinating account" The Bulletin Of The Royal College Of PathologistsTable of ContentsList of Illustrations Foreword by Carmen Hooker Odom and Samuel L. Milbank Foreword by David J. Sencer Preface Part One. Africa: Identifying the Key Strategy 1. A Loathsome Disease 2. A Succession of Mentors 3. Practicing Public Health in Nigeria 4. Fire Line around a Virus 5. Extinguishing Smallpox in a Time of War Part Two. India: Meeting the Challenge of Eradication 6. Under the Rule of Variola 7. Unwarranted Optimism 8. A Gorgeous Coalition 9. Rising Numbers, Refining Strategy 10. Water on a Burning House 11. Smallpox Zero Conclusion Postscript Appendix: A Plan in the Event of Smallpox Bioterrorism Notes Glossary Index

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Book SynopsisTells how smallpox, a disease that killed, blinded, and scarred millions over centuries of human history, was completely eradicated in a spectacular triumph of medicine and public health. This title details the remarkable program that involved people from countries around the world in pursuit of a single objective - eliminating smallpox.Trade Review"Dr. Foege's book ... remind[s] us how fragile life looks." New York Times "Bounces the reader along with him in his jeep, on motorbikes over rugged terrain and on bustling trains... (And) shows what can be accomplished when governments and thousands of health workers focus on a single objective. " Wall Street Journal "[Foege] writes a mixture of memoir, dry public health guide and riveting tale of an all-consuming mission." -- Tiffany O'Callaghan New Scientist "A readable and thorough account by a key player in this outstanding victory for public health." Library Journal "A reminder of the importance of preventive medicine." Jama "A great, quick, and intensely personal read about the inside story of Foege's revolutionary idea and powerful actions... Foege was wise before his time." Medpage Today "Demonstrate[s] the enormous benefit that can accrue to mankind when a determined and ambitious band of individuals come together." The Lancet "Gives an intimate sense of what it is like to work on the ground in some of the world's most impoverished countries -- and tells what it is like to contribute to programs that really do change the world." Scienceblogs.com/The Guardian "Inspiring... A fascinating human interest account that is expertly merged with scientific facts." -- Pascal James Imperato Jrnl Of Community Health "A fascinating account" The Bulletin Of The Royal College Of PathologistsTable of ContentsList of Illustrations Foreword by Carmen Hooker Odom and Samuel L. Milbank Foreword by David J. Sencer Preface Part One. Africa: Identifying the Key Strategy 1. A Loathsome Disease 2. A Succession of Mentors 3. Practicing Public Health in Nigeria 4. Fire Line around a Virus 5. Extinguishing Smallpox in a Time of War Part Two. India: Meeting the Challenge of Eradication 6. Under the Rule of Variola 7. Unwarranted Optimism 8. A Gorgeous Coalition 9. Rising Numbers, Refining Strategy 10. Water on a Burning House 11. Smallpox Zero Conclusion Postscript Appendix: A Plan in the Event of Smallpox Bioterrorism Notes Glossary Index

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Book SynopsisIn 1969 the first edition of this book introduced the concepts of statistics and their medical application to readers with no formal training in this area. While retaining this basic aim, the authors have expanded the coverage in each subsequent edition to keep pace with the increasing use and sophistication of statistics in medical research.Trade Review"Medical Uses of Statistics, 3rd Edition" presents the concepts of medical statistics across a broad range of topics with a practical perspective, a moderate level of detail, and a minimal number of formulae . . . The text is clearly written in a consistent style, as illustrated by these excerpts." (Journal of Clinical Research Best Practices, 8 August 2011) ...the book can be warmly recommended as a textbook and reference for undergraduate medical students and health professionals." Statistical Methods in Medical Research (on the fourth edition) "It should prove useful for both undergraduate medical and paramedical students, and for postgraduate researchers and clinicians who wish to have a greater understanding of medical statistics or to analyse their own data." Modern Medicine of Australia (on the fourth edition)Table of Contents1. Describing Data - A Single Variable. 2. Probability, Populations and Samples. 3. Associations: Chance, Confounded or Causal?. 4. Confidence intervals: General principles; Proportions, Means, Medians, Counts and Rates. 5. Hypothesis Testing: General Principles and One-sample Tests for Means, Proportions, Counts and Rates. 6. Epidemiological and Clinical Research Methods. 7. Confidence intervals and Hypothesis Tests: Two-group Comparisons. 8. Sample Size Determination. 9. Comparison of More than Two Independent Groups. 10. Associations between Two Quantitative Variables: Regression and Correlation. 11. Multivariate Analysis and the Control of Confounding!. 12. Bias and Measurement Error. Bibliography. Appendix A Computational Shortcuts. Appendix B Statistical Tables. Appendix C A "Cookbook" of Hypothesis Tests and Confidence Intervals. Appendix D World Medical Association Declaration of Helsinki

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Book Synopsisaeo An encyclopaedic reference text for all aspects of advanced medical statistics aeo Special regression problems have been added to the new edition aeo ". the standard text for professional medical statisticians. " -- Aslib Book Guide aeo new chapter on laboratory assays.Trade ReviewOn the fourth edition: '...this breakthrough revision of a classic...is truly excellent: comprehensive, informative, able to be read at a variety of levels by a variety of readers, modern and insightful.' Statistics in Medicine, Volume 22, 2003 '...this is a volume which could usefully, and perhaps should, be read from cover to cover by anyone embarking on the study of medical statistics. For those already working in the area, it should at least be on their bookshelves.' Short Book Reviews, Volume 22, Number 2, August 2002 '...each edition has improved and expanded considerably on the last, keeping pace with the ever-changing field of medical statistics...' eMJA Bookroom, 2002 On previous editions: '...this is an excellent book...I strongly recommend this book...' International Society for Clinical Biostatistics, December 1997 '...this classical beauty has aged well.' International Statistical Institute, April 1996 '...readers who...use statistical analysis...must buy this third edition' Australian-New Zealand Journal of Surgery, Spring 1995 '...the standard text for professional medical statisticians.' Aslib Book Guide, November 1994Table of ContentsPreface to the fourth edition. 1 The Scope of Statistics. 2 Describing Data. 3 Probability. 4 Analysing Means and Proportions. 5 Analysing Variances, Counts and Other Measures. 6 Bayesian Methods. 7 Regression and Correlation. 8 Comparison of Several Groups. 9 Experimental Design. 10 Analysing Non-Normal Data. 11 Modelling Continous Data. 12 Further Regresson Models for a Continuous Response. 13 Multivariate Methods. 14 Modelling Categorical Data. 15 Empirical Methods for Categrorical Data. 16 Further Bayesian Methods. 17 Survival Analysis. 18 Clinical Trials. 19 Statistical Methods in Epidemiology. 20 Laboratory Assays. Appendix tables. References. Author Index. Subject Index.

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Book SynopsisIn the year 2000 the World Health Organization estimated that 85 percent of fifteen-year-olds in Botswana would eventually die of AIDS. This title tells the true story of lives ravaged by AIDS - of orphans, bereaved parents, and widows; and, of families who devote most Saturdays to the burial of relatives and friends.Trade ReviewThis is a remarkable account of the human effect of a pandemic, written by two people with an intimate knowledge of Botswana and its struggle to deal with AIDS. I recommend this book most warmly for its humanity and insight. -- Alexander McCall SmithThis extraordinary book brings to life the utterly unique stories of people in Botswana; yet the fact is that struggle, suffering and redemption are also universal stories with which we can all identify. The partnership of Dow and Essex, storyteller and scientist, results in a precious alchemy: a book that is engrossing, transforming and an important addition to the canon of the literature of HIV. -- Abraham Verghese, author of Cutting for Stone and My Own CountryThis is the AIDS book to read—first, because of its novel approach of describing true and very moving stories of the Botswana experience, coupled with lucid and relevant scientific explanations fitting for each of the stories, and second, because of the experience and caliber of its authors. Saturday Is For Funerals is at once highly moving, while providing unforgettable lessons from the greatest pandemic in medical history. Unity Dow knows her people and their tragic stories, and as we would expect from a highly regarded novelist, displays these stories with grace and beauty. Coauthor Professor Max Essex has as much or more public health scientific experience and more insights into HIV/AIDS than anyone I know in the world. This book would be valuable not only for people impacted by HIV, but also for politicians, educators, students, and anyone who wants an education on mankind's greatest 'plague.' -- Robert C. Gallo, M.D., Director, Institute of Human Virology, University of Maryland School of MedicineThis wonderful book is an inspiration to anyone who wants to learn more about the HIV/AIDS epidemic and its impact on Africa. The authors have collaborated on a well-written tome that is highly informative yet easy to read and digest. This book will have to be considered for a Pulitzer Prize and other suitable recognition. -- Mark A. Wainberg, President Emeritus, International AIDS SocietyUnity Dow and Max Essex have crafted an extraordinarily effective synergy of science and societal journalism. Saturday Is For Funerals explores the fragility and resilience of human spirit through poignant personal narratives around courtships, young love, and family tradition, centered in the Botswana 'hot zone' of the most devastating epidemic in recorded history. In conversational and gripping prose Saturday Is For Funerals engages as it informs, standing alongside Randy Shilts (And the Band Played On) and Abraham Verghese (My Own Country) as a heartfelt chronicle of the turbulent times that AIDS has engendered for global society, for science, and for amazing African peoples. -- Stephen J. O'Brien, AIDS researcher, author of Tears of the Cheetah: And Other Tales from the Genetic FrontierThe HIV/AIDS epidemic in Botswana is explored with sensitivity and scientific rigor in this heartening book...This richly informative book dispels much of the mystery still surrounding HIV/AIDS, revealing how life goes on for those infected. Readers overwhelmed by (and even numbed to) the images of desolation that accompany coverage of the epidemic will find a realistic but optimistic assessment of a society successfully tackling the problem and a model for other afflicted nations. * Publishers Weekly *The narratives provide a human touch and convincingly illustrate the tremendous impact of AIDS on women, children, infants, friends, family, and culture. While Botswana was hard-hit by the AIDS epidemic, it has provided a successful model for other countries by taking a proactive approach to dealing with the disease. -- Tina Neville * Library Journal *A decade ago, the AIDS epidemic in the southern African country had gotten so bad that leaders feared its people were in danger of extinction; the World Health Organization estimated that 85 percent of 15 year olds would eventually die of the disease. Today, Botswana is the pride of Africa. The country's remarkable journey is detailed in Saturday Is for Funerals, a new book by renowned AIDS activist Unity Dow and researcher Max Essex. Weaving together personal anecdotes and medical history, the authors reveal how a combination of proactive government intervention, education, research, and foreign aid have achieved the near impossible...Bringing Saturday Is for Funerals to life--and distinguishing it from other books about AIDS in Africa--are its first-hand, often heart-wrenching stories of the epidemic's victims...[Dow] shares evocative stories of marriages torn apart by the disease, and saved through drug therapy, of tribal leaders encouraging circumcision to reduce infection, and of AIDS orphans. -- Danielle Friedman * Daily Beast *Unity Dow, a judge of the Interim Independent Constitutional Dispute Resolution Court of Kenya, and Max Essex, a Harvard professor of health sciences, have worked at the Botswana-Harvard Partnership to control, contain, and curtail the HIV/AIDS epidemic that has devastated Botswana. In this informative book, they present the many difficulties they face--medical, cultural, psychological, and financial. -- Barbara Fisher * Boston Globe *The epidemic of HIV and AIDS marching across Africa is threatening to crush entire countries under its weight. Saturday Is for Funerals tells the story of how one country, Botswana, is stemming the epidemic with bold political leadership, a strategic and scientific approach, and more than a little grit. -- Priya Shetty * New Scientist *The book is compelling because it tells us the real stories of people living with HIV/Aids and the devastating effects it has on families. There are stories of deadly sexual betrayal and bitterness, but also resilience, caring and kindness...This hook is then used to engage the reader and explain the science behind the disease in a generally accessible way. It is a work of both literature and science and works brilliantly. -- Pádraig Carmody * Irish Times *A compelling look at the toll of AIDS in Africa and some hopeful developments. -- Vanessa Bush * Booklist *Tragic and heartwrenching stories of victims, coupled with scientific explanations, are effectively woven into chapters on mother-to-child transmission, fear of diagnosis, AIDS in children, highly active antiretroviral therapy, drug resistance and toxicities, stigma, and orphans. The book comes at a critical time as news of HIV/AIDS "donor fatigue" makes headlines, and funding to battle AIDS in Africa is shrinking. This is very important reading for politicians, educators, students, and those seeking an education on humankind's greatest plague. -- P. Wermager * Choice *Dow and Essex bring their distinct and complementary knowledge of HIV infection in southern Africa into a book that effectively depicts both the personal and the scientific facets of the Botswana AIDS epidemic...The science is competently explained in terms that a lay person could understand, and the combination works well, making this book a good introduction to the key facts about HIV/AIDS as well as a moving depiction of the individual tragedies this disease can inflict...This book would be worthwhile reading for people who want to learn more about the HIV epidemic but would never pick up a textbook or scientific article...In my view, this book should be compulsory reading for policy makers and leaders throughout Africa, who often appear to be unaccountably remote from the suffering of ordinary people in their countries. -- Sarah Rowland-Jones * Nature Medicine *Unity Dow and Max Essex illuminate the AIDS epidemic in sub-Saharan Africa by reporting on its consequences for the lives of those living in a single country, Botswana. Dow is a human rights lawyer and judge. Essex is an AIDS scientist at Harvard University. They have deployed their complementary experiences to examine multiple aspects of AIDS, dividing each chapter in half. Dow describes the personal stories of those affected by AIDS. She creates play scripts of conversation to situate the issue at hand--AIDS among children, access to medicines, fear and stigma, diagnosis--in a context that illustrates the intimacy and tragedy of the epidemic. Essex follows up with a scientific explanation of the preceding drama, together with his own reflections abpout what is being done to prevent such an episode from happening again. It is an effective strategy, drawing the reader into the particular culture of AIDS in Botswana, while showing what the global medical research enterprise into HIV can deliver for people who live in often excruciating poverty. -- Richard Horton * Times Literary Supplement *Table of Contents* Preface * Introduction *1. A Family of Funerals: The Epidemic *2. I Know You Still Love Me: Sexual Transmission *3. Masego and Katlego: Mother-to-Child Transmission *4. Mandla Gets Tested: Diagnosis of HIV Infection *5. The Death of Mma Monica: AIDS Disease in Adults and Availability of Treatment *6. Naledi and Her Nephew Shima: AIDS in Children *7. It Is the Will of God: HIV and Tuberculosis *8. Walking Skeletons and Hesitant Hugs: Toxicities and Resistance to Drugs Used to Treat HIV/AIDS *9. The Page Is Turning Red: Blood Transfusion as a Risk for HIV Infection *10. A Tribal Tradition: Male Circumcision to Prevent HIV Infection *11. A Matter of Commitment: Development of an HIV Vaccine *12. Ancestral Control: Evil Spirits and HIV as the Cause of AIDS *13. He Died in China: Fear and Stigma *14. Opelo's Rebellion: Issues of Adolescents and Women *15. Desperation for Pono: Orphans of HIV/AIDS *16. Government Action Makes a Difference: A Nation Responds * Glossary * Further Reading * Index

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Book SynopsisThis perennial bestseller is an ideal introductions to epidemiology in health care. The fifith editon retains the book's simplicity and brevity, at the same time providing the reader with the core elements of epidemiology needed in health care practice and research. The text has been revised throughout, with new examples introduced to bring the book right up to date.Table of Contents1 What is Epidemiology?. 2 Quantifiying Disease in Populations. 3 Comparing Disease Rates. 4 Measurement Error and Bias. 5 Planning and Conducting a Survey. 6 Ecological Studies. 7 Longitudinal Studies. 8 Case-control and Cross Sectional Studies. 9 Experimental Studies. 10 Screening. 11 Outbreaks of Disease. 12 Reading Epidemiolgical Reports. Further Reading. Index

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Book SynopsisThe role that the social and behavioural sciences play in the daily practice of dentistry is now an essential part of all dentistry training, but it can often seem distant from the reality of daily clinical practice.Trade Review�This new book represents a pioneering effort to bring important selected topics and practical examples from sociology and psychology to students of dentistry. Having participated in such a course at my own university, I can highly recommend this book.�William C. Cockerham, University of Alabama at Birmingham �This book comprehensively and critically discusses the application of health psychology and health sociology concepts to oral health. It is essential reading for oral health professionals and will help them introduce behavioural sciences in their everyday practice and also facilitate better understanding of the overall context of oral health care provision.�Georgios Tsakos, University College LondonTable of ContentsIntroductionChapter 1: The Social Context of Oral Health and DiseaseChapter 2: Poverty, Inequality and Oral HealthChapter 3: Gender and Oral HealthChapter 4: Ethnicity and Oral HealthChapter 5: Oral Health in Later LifeChapter 6: Disability and Oral HealthChapter 7: Symptoms and Help-SeekingChapter 8: Adherence and Behaviour Change in Dental SettingsChapter 9: Stress and HealthChapter 10: Issues in Social PsychologyChapter 11: Pain and Dental AnxietyChapter 12: Communication in the Dental SurgeryChapter 13: The Dentist in SocietyReferences

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Book SynopsisDuBos et. al. examine the social aspects of the TB epidemic, along with some of the biological factors. They show how TB was romaticized, how it was portrayed as a demon coming to rob the healthy of life, and how it sparked scientific invention - in particular the stethescope. The introduction is wonderful as it lays out the basic parts of the book.Table of ContentsForeword by David Mechanic Introductory Essay: Dubos and Tuberculosis, Master Teachers by Barbara Gutmann Rosenkrantz To Our Sources Introduction to the First Edition Part One: The White Plague in the Nineteenth Century I The Captain of All the Men of Death II Death Warrant for Keats III Flight from the North Winds IV Contagion and Heredity V Consumption and the Romantic Age Part Two: The Causes of Tuberculosis VI Phthisis, Consumption and Tubercles VII Percussion, Auscultation and the Unitarian Theory VIII The Germ Theory of Tuberculosis IX Infection and Disease Part Three: Cure and Prevention of Tuberculosis X The Evaluation of Therapeutic Procedures XI Treatment and Natural Resistance XII Drugs, Vaccines and Public Health Measures XIII Healthy Living and Sanatoria Part Four: Tuberculosis and Society XIV The Evolution of Epidemics XV Tuberculosis and Industrial Civilization XVI Tuberculosis and Social Technology Appendices Bibliography and Notes Index

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Book SynopsisComprehensive guide to biostatistics Draws on examples from dentistry and oral healthcare research Encourages intuitive understanding of statistical concepts Includes glossary of definitions and notation.Trade Review"This is a book of massive erudition, of great value to the career statistician but not much help to those of us who rank statistics lower in our priorities." (Primary Dental Care and Team in Practice, 1 April 2011)Table of ContentsChapter 1. Introduction. 1. What Is Biostatistics?. 2. Why Do I Need Statistics?. 3. How Much Mathematics Do I Need?. 4. How to Study Statistics?. 5. Reference. Chapter 2. Summarizing Data. 1. Raw Data and Basic Terminology. 2. The Levels of Measurements. 3. Frequency Distributions. Frequency Tables. Relative Frequency. 4. Graphs. Bar Graphs. Pie Charts. Line Graphs. Histograms. Stem and Leaf Plots. 5. Clinical Trials. 6. Confounding Variables. 7. Exercises. 8. References. Chapter 3. Measures of Central Tendency, Dispersion, and Skewness. 1. Introduction. 2. Mean. 3. Weighted Mean. 4. Median. 5. Mode. 6. Geometric Mean. 7. Harmonic Mean. 8. Mean and Median of Grouped Data. 9. Mean of Two or More Means. 10. Range. 11. Percentiles and Interquartile Range. 12. Box-whisker Plot. 13. Variance and Standard Deviation. 14. Coefficient of Variation. 15. Variance of the Grouped Data. 16. Skewness. 17. Exercises. 18. References. Chapter 4. Probability. 1. Introduction. 2. Sample Space and Events. 3. Basic Properties of Probability. 4. Independence and Mutually Exclusive Events. 5. Conditional Probability. 6. Bayes Theorem. 7. Rates and Proportions. Prevalence and Incidence. Sensitivity and Specificity. Relative Risk and Odds Ratio. 8. Exercises. 9. References. Chapter 5. Probability Distributions. 1. Introduction. 2. Binomial Distribution. 3. Poisson Distribution. 4. Poisson Approximation to Binomial Distribution. 5. Normal Distribution. Properties of Normal Distributions. Standard Normal Distribution. Using Normal Probability Table. Further Applications of Normal Probability. Normal Approximation to the Binomial Distribution. 6. Exercises. 7. References. Chapter 6. Sampling Distributions. 1. Introduction. 2. Sampling Distribution of the Mean. Standard Error of the Sample Mean. Central Limit Theorem. 3. Student's t Distribution. 4. Exercises. 5. References. Chapter 7. Confidence Intervals and Sample Size. 1. Introduction. 2. Confidence Intervals for the Mean and Sample Size n when Is Known. 3. Confidence Intervals for the Mean when is Not Known. 4. Confidence Intervals for the Binomial Parameter p. 5. Confidence Intervals for the Variances and Standard Deviations. 6. Exercises. 7. References. Chapter 8. Hypothesis Testing: One Sample Case. 1. Introduction. 2. Concept of Hypothesis Testing. 3. One-tailed Z Test of the Mean of a Normal Distribution When Is Known. 4. Two-tailed Z Test of the Mean of a Normal Distribution When Is Known. 5. t Test of the Mean of a Normal Distribution. 6. The Power of a Test and Sample Size. 7. One-Sample Test for a Binomial Proportion. 8. One-Sample Test for the Variance of a Normal Distribution. 9. Exercises. 10. References. Chapter 9. Hypothesis Testing: Two-Sample Case. 1. Introduction. 2. Two Sample Z Test for Comparing Two Means. 3. Two Sample t Test for Comparing Two Means with Equal Variances. 4. Two Sample t Test for Comparing Two Means with Unequal Variances. 5. The Paired t Test. 6. Z Test for Comparing Two Binomial Proportions. 7. The Sample Size and Power of a Two Sample Test. Estimation of a Sample Size. The Power of a Two Sample Test. 8. The F Test for the Equality of Two Variances. 9. Exercises. 10. References. Chapter 10. Categorical Data Analysis. 1. Introduction. 2. 2 x 2 Contingency Table. 3. r x c Contingency Table. 4. The Cochran-Mantel-Haenszel Test. 5. The McNemar Test. 6. The Kappa Statistic. 7. Goodness of Fit Test. 8. Exercises. 9. References. Chapter 11. Regression Analysis and Correlation. 1. Introduction. 2. Simple Linear Regression. Description of Regression Model. Estimation of Regression Function. Aptness of a Model. 3. Correlation Coefficient. Significance of Correlation Coefficient. 4. Coefficient of Determination. 5. Multiple Regression. 6. Logistic Regression. The Logistic Regression Model. Fitting the Logistic Regression Model. 7. Multiple Logistic Regression Model. 8. Exercises. 9. References. Chapter 12. One-Way Analysis of Variance. 1. Introduction. 2. Factors and Factor Levels. 3. Statement of the Problem and Model Assumptions. 4. Basic Concepts in ANOVA. 5. F-test for Comparison of k Population Means. 6. Multiple Comparisons Procedures. Least Significant Difference Method. Bonferroni Approach. Scheffe's Method. Tukey's Procedure. 7. One-way ANOVA Random Effects Model. 8. Test for Equality of k Variances. Bartlett's Test. Hartley's Test. 9. Exercises. 10. References. Chapter 13. Two-Way Analysis of Variance. 1. Introduction. 2. General Model. 3. Sum of Squares and Degrees of Freedom. 4. F Test. 5. Exercises. 6. References. Chapter 14. Non-Parametric Statistics. 1. Introduction. 2. The Sign Test. 3. The Wilcoxon Rank Sum Test. 4. The Wilcoxon Signed Rank Test. 5. The Median Test. 6. The Kruskal-Wallis Test. 7. The Friedman Test. 8. The Permutation Test. 9. The Cochran Test. 10. The Squared Rank Test For Variances. 11. Spearman's Rank Correlation Coefficient. 12. Exercises. 13. References. Chapter 15. Survival Analysis. 1. Introduction. 2. Person-Time Method and Mortality Rate. 3. Life Table Analysis. 4. Hazard Function. 5. Kaplan-Meier Product Limit Estimator. 6. Comparing Survival Functions. Gehan's Generalized Wilcoxon Test. The Logrank Test. The Mantel and Haenszel Test. 7. Piecewise Exponential Estimator (PEXE). Small Sample Illustration. General Description of PEXE. An Example. Properties of PEXE and Comparisons with Kaplan-Meier Estimator. 8. References. Appendix. Solutions to Selected Exercises. Table A. Table of Random Numbers. Table B. Table of Binomial Probabilities. Table C. Table of Poisson Probabilities. Table D. Standard Normal Probabilities. Table E. Percentiles of the t Distribution. Table F. Percentiles of the Distribution. Table G. Percentiles of the F Distribution

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Book SynopsisOffers a chronological narrative of how six epidemics transformed the American urban landscape, reflecting changing views of the power of design, pathology of disease, and the epidemiology of the environment.Trade ReviewA substantial contribution to the field illustrating how public health and planning policies merged and supported each other after the Industrial Revolution, parted ways in the twentieth century, and have now remerged in tackling contemporary issues of health and the built environment. Carr draws on a myriad of sources, and the work represents sound and thorough scholarship." —Clare Cooper Marcus, University of California, Berkeley, author of Iona Dreaming: The Healing Power of Place"I cannot imagine a more perfect post-pandemic book. Public health provides the legal foundations for the architecture, landscape architecture, and planning professions in the United States. As a result, it is essential to understand the role that public health has played in shaping our cities. In The Topography of Wellness, Sara Jensen Carr provides a tour-de-force review and analysis of the checkered history of the contributions that public health and disease have played in designing and planning the American landscape." —Frederick Steiner, University of Pennsylvania Weitzman School of Design, author of Making Plans: How to Engage with Landscape, Design, and the Urban Environment

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Book SynopsisGlobal Health in Africa is a first exploration of selected histories of global health initiatives in Africa. The collection addresses some of the most important interventions in disease control, including mass vaccination, large-scale treatment and/or prophylaxis campaigns, harm reduction efforts, and nutritional and virological research.Trade Review“An immensely valuable collection…Global Health in Africa should inspire a new generation of local historians to locate the medical in African histories.” * Social History of Medicine *“For anyone looking for a book to assign to undergraduates, or to recommend to students who are interested in the field of global health, the collection edited by Giles-Vernick and Webb, Global Health in Africa, is [an] obvious choice.” * African Studies Review *“Taken as a collective, the essays offer other lessons to those interested in African public and global health. The most striking theme across the volume are the ways in which health interventions can unintentionally contribute to ill health and create tense relationships with medical practitioners.… A second theme is how individual rights are frequently imperiled by mass campaigns, particularly ones where the line between cure and prevention is blurred.… The collection makes the case well for including historical perspectives in approaching global health, but it also demonstrates how including a global health frame can contribute to histories of disease, health and healing in Africa.” * H-Net *“The distinctive contribution of the work is its explicit historical orientation…. Importantly, the historical perspective…highlights the long-term continuities, unquestioned assumptions and moral ambiguities that characterize global health initiatives in Africa. The breadth and depth of the contributions ensures that the book comes a long way in achieving its objective to contribute to the development of a new field of global health history.” * Comparativ *“This volume illustrates very well that the current day applicability of the core concepts of global health [have] need of the serious critical historical and cultural examination that this volume (and no others that I know of) now provides in its richest and most useful form.”“[Global Health in Africa] demonstrates that Africa’s global health history is rich, important, and under-researched. The strength of this book lies in the breadth and depth of the studies presented in one volume.”“Provides a variety of case studies from different parts of the continent and different historical periods.… The cumulative effect of the chapters impresses on the reader the scope of the experimentation that has been done and that continues to be done on African bodies.”

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Book SynopsisThis captivating study tells Mexico's best untold stories. The book takes the devastating 1833 cholera epidemic as its dramatic centre and expands beyond this episode to explore love, lust, lies, and midwives.

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Book SynopsisOffers an introduction to behavioral and social science research methods in the health sciences. This book provides complete coverage of the process behind these research methods, including information-gathering, decision formation, and results presentation.Table of ContentsPreface ix Part One Overview of the Research Process 1 Behavioral and Social Research in the Health Sciences 3 2 Ethics and Research 25 3 The Foundations of Research 45 4 An Overview of Empirical Methods 79 Part Two Nuts and Bolts of Research 5 Writing the Research Report 113 6 Reviewing the Literature 139 7 Sampling 161 8 Assessments, Surveys, and Objective Measurement 191 9 A Model for Research Design 225 Part Three Common Research Designs 10 Correlational Research 255 11 Between-Subjects Designs 285 12 Single-Variable Between-Subjects Research 315 13 Between-Subjects Factorial Designs 345 14 Correlated-Groups Designs 367 Part Four Special Research Designs 15 Single-Participant Experiments, Longitudinal Studies, and Quasi-Experimental Designs 393 16 Research with Categorical Data 415 17 Qualitative and Mixed-Methods Research 439 Appendix A Reviewing the Statistics behind the Research 461 Appendix B Statistical Tables 479 Index 521

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Book SynopsisMethods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods includes updates of established literature from the Wiley Encyclopedia of Clinical Trials as well as original material based on the latest developments in clinical trials. Prepared by a leading expert, the second volume includes numerous contributions from current prominent experts in the field of medical research. In addition, the volume features: Multiple new articles exploring emerging topics, such as evaluation methods with threshold, empirical likelihood methods, nonparametric ROC analysis, over- and under-dispersed models, and multi-armed bandit problems Up-to-date research on the Cox proportional hazard model, frailty models, trial reports, intrarater reliability, conditional power, and the kappa index Key qualitative issues including cost-effectiveness analysis, publication bias, and regulatory issues, which are crucial to the planniTrade Review“This book provides a good overview on most relevant topics for clinical trials.” (Biometrical Journal, 1 October 2015) Table of ContentsContributors xix Preface xxiii 1 Analysis of Over- and Underdispersed Data 1 2 Analysis of Variance (ANOVA) 10 3 Assessment of Health-Related Quality of Life 26 4 Bandit Processes and Response-Adaptive Clinical Trials: The Art of Exploration Versus Exploitation 40 5 Bayesian Dose-Finding Designs in Healthy Volunteers 51 6 Bootstrap 62 7 Conditional Power in Clinical Trial Monitoring 102 8 Cost-Effectiveness Analysis 111 9 Cox-Type Proportional Hazards Models 126 10 Empirical Likelihood Methods in Clinical Experiments 146 11 Frailty Models 166 12 Futility Analysis 174 13 Imaging Science in Medicine I: Overview 187 14 Imaging Science in Medicine, II: Basics of X-Ray Imaging 213 15 Imaging Science in Medicine, III: Digital (21st Century) X-Ray Imaging 264 16 Intention-to-Treat Analysis 313 17 Interim Analyses 323 18 Interrater Reliability 334 19 Intrarater Reliability 340 20 Kaplan-Meier Plot 357 21 Logistic Regression 365 22 Metadata 380 23 Microarray 392 24 Multi-Armed Bandits, Gittins Index, and Its Calculation 416 25 Multiple Comparisons 436 26 Multiple Evaluators 446 27 Noncompartmental Analysis 457 28 Nonparametric ROC Analysis for Diagnostic Trials 483 29 Optimal Biological Dose for Molecularly Targeted Therapies 496 30 Over- and Underdispersion Models 506 31 Permutation Tests in Clinical Trials 527 32 Pharmacoepidemiology, Overview 536 33 Population Pharmacokinetic and Pharmacodynamic Methods 551 34 Proportions: Inferences and Comparisons 570 35 Publication Bias 595 36 Quality of Life 608 37 Relative Risk Modeing 622 38 Sample Size Considerations for Morbidity/Mortality Trials 633 39 Sample Size for Comparing Means 642 40 Sample Size for Comparing Proportions 653 41 Sample Size for Comparing Time-to-Event Data 664 42 Sample Size for Comparing Variabilities 672 43 Screening, Models of 689 44 Screening Trials 721 45 Secondary Efficacy End Points 731 46 Sensitivity, Specificity, and Receiver Operator Characteristic (ROC) Methods 740 47 Software for Genetics/Genomics 752 48 Stability Study Designs 778 49 Subgroup Analysis 793 50 Survival Analysis, Overview 802 51 The FDA and Regulatory Issues 815 52 The Kappa Index 836 53 Treatment Interruption 846 54 Trial Reports: Improving Reporting, Minimizing Bias, and Producing Better Evidence-Based Practice 860 55 U.S. Department of Veterans Affairs Cooperative Studies Program 876 56 Women's Health Initiative: Statistical Aspects and Selected Early Results 901 57 World Health Organization (WHO): Global Health Situation 914 Index 925

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Book SynopsisA global perspective on the management and prevention of emerging and re-emerging diseases Emerging infectious diseases are newly identified or otherwise previously unknown infections that cause public health challenges. Re-emerging infectious diseases are due to both the reappearance of and an increase in the number of infections from a disease that is known, but which had formerly caused so few infections that it was no longer considered a public health problem. The factors that cause the emergence or re-emergence of a disease are diverse. This book takes a look at the world''s emerging and re-emerging diseases. It covers the diagnosis, therapy, prevention, and control of a variety of individual diseases, and examines the social and behavioral issues that could contribute to epidemics. Each chapter focuses on an individual disease and provides scientific background and social history as well as the current basics of infection, epidemiology, and control. Table of ContentsPreface xv Acknowledgments xvii Chapter 1 Prologue 3 Introduction 3 Causative Factors 7 Salient Features 11 Emerging Epidemics 11 Re-Emerging Epidemics 17 Antimicrobial Resistance 18 Public Health Implications 20 References 22 Chapter 2 Epidemics Fundamentals 24 Introduction 24 Definitions 24 Types of Epidemics 26 Epidemiological Triad 29 Forecasting an Epidemic 31 Contingency Plan 33 Investigation of Epidemics 35 Management of Epidemics 38 Control of Epidemics 39 Principles of Planning Emergency Services 41 References 44 Chapter 3 Disasters and Epidemics 46 Fundamentals 46 Contributory Factors 51 Investigation of Rumors 52 References 54 Chapter 4 Biosafety 56 Introduction 56 Components 57 Hand Washing 61 Preventing Needlestick Injuries 62 Safe Transport of Biological Material 64 Safe Decontamination of Spills 65 Safe Handling of Dead Bodies 67 Personal Protective Equipment 69 Management of Biomedical Waste 69 Infection Control Check List 71 Biosafety Levels 71 Accreditation of Hospitals and Laboratories 74 References 75 Chapter 5 Tuberculosis 76 History 76 Magnitude 78 Agent Factors 81 Host Factors and High-Risk Groups 88 Environmental Factors 89 Modes of Transmission 90 Pathology and Immunology 91 Clinical Manifestations 96 Diagnosis of Tuberculosis 101 Directly Observed Treatment, Short Course (DOTS) 119 Tuberculosis and HIV 135 Drug-Resistant Tuberculosis 140 Prevention and Control 142 Social and Cultural Factors 145 References 148 Chapter 6 Plague 154 History of Plague 154 Magnitude 155 Agent Factors 157 Host Factors 158 Environmental Factors 159 Reservoir 159 Mode of Transmission 160 Clinical Manifestations 161 Laboratory Diagnosis 164 Differential Diagnosis 170 Clinical Management 170 Prevention and Control 172 References 174 Chapter 7 Leptospirosis 176 Introduction 176 Magnitude 176 Agent Factors 177 Host Factors 179 Environmental Factors 182 Mode of Transmission 183 Pathology and Immunology 183 Clinical Manifestations 185 Laboratory Diagnosis 189 Clinical Management 206 Surveillance 210 Investigation of an Outbreak 211 Prevention and Control 213 References 217 Chapter 8 Dengue 220 Introduction 220 Magnitude 221 Agent Factors 222 Host Factors 228 Environmental Factors 230 Vector Biology 231 Clinical Features 235 Laboratory Diagnosis 244 Immune Response to Dengue Virus 245 Clinical Management 248 Investigation of Outbreaks 253 Prevention and Control 257 References 259 Chapter 9 Japanese Encephalitis 263 History 263 Magnitude of the Problem 264 Epidemiology 266 Vector Biology 270 Clinical Features 270 Differential Diagnosis 271 Laboratory Diagnosis 272 Case Management 275 Prevention and Control 278 References 280 Chapter 10 Chikungunya Fever 283 Introduction 283 Epidemiology 284 The Chikungunya Virus 284 Clinical Features 291 Laboratory Diagnosis 298 Differential Diagnosis 300 Clinical Management 301 Investigation of Outbreaks 306 Treatment 307 Prevention and Control 308 References 311 Chapter 11 West Nile Fever 316 Epidemiology 316 Global Scenario 317 The Etiological Agent 318 Clinical Features 322 Laboratory Diagnosis 324 Clinical Management 326 Investigation of Outbreaks 327 Prevention and Control 330 References 336 Chapter 12 Chandipura Virus Encephalitis 340 Epidemiology 340 The Chandipura Virus 341 Clinical Features 350 Laboratory Diagnosis 351 Differential Diagnosis 353 Clinical Management 354 Investigation of Outbreaks 356 Prevention and Control 358 References 359 Chapter 13 Kyasanur Forest Disease 361 Introduction 361 Epidemiology 362 Vector Biology 363 Clinical Features 366 Differential Diagnosis 366 Laboratory Diagnosis 368 Case Management 369 Prevention and Control 371 References 374 Chapter 14 Hantavirus Disease 375 Introduction 375 Epidemiology and Global Scenario 376 The Etiological Agent 381 Clinical Features 387 Differential Diagnosis 390 Laboratory Diagnosis 390 Case Management 393 Prevention and Control 394 References 396 Chapter 15 Influenza 400 Historical Aspects 400 Global Scenario 402 Agent Factors 405 Host Factors 418 Environmental Factors 419 Mode of Transmission 421 Clinical Manifestations 422 Immune Response to Influenza 424 Laboratory Diagnosis 427 Clinical Management 431 Surveillance 434 Investigation of an Outbreak 437 Prevention and Control 441 Avian Influenza 445 Swine Influenza 447 References 450 Chapter 16 Severe Acute Respiratory Syndrome 455 Introduction 455 Epidemiology 455 Causative Agent 456 Transmission of Severe Acute Respiratory Syndrome Virus 456 Clinical Features 457 Laboratory Diagnosis 457 Treatment 458 Prevention and Control 461 References 461 Chapter 17 Nipah Virus 462 Introduction 462 Epidemiology 463 Etiological Agent 463 Transmission 463 Clinical Features 466 Laboratory Diagnosis 467 Prevention and Control 468 References 469 Chapter 18 Paragonimiasis 470 Magnitude of the Problem 470 The Parasite: Paragonimus 471 Epidemiology 475 Clinical Manifestations 478 Radiological Features of Paragonimiasis 481 Laboratory Diagnosis 484 Differential Diagnosis 487 Clinical Management 487 Public Health Importance 488 References 489 Chapter 19 Melioidosis 492 Introduction 492 Epidemiology 493 The Etiological Agent: Burkholderia pseudomallei 495 Clinical Manifestations 498 Laboratory Diagnosis 501 Clinical Management 503 Investigation of an Outbreak 504 Prevention and Control of Melioidosis 506 Public Health Importance 508 References 508 Chapter 20 Biowarfare and Bioterrorism 513 Introduction 513 Historical Aspects 514 Potential Agents 519 Epidemiological Clues 545 Laboratory Diagnosis 548 Clinical Management 566 Biosurveillance 568 Investigation of an Outbreak 573 Preparedness and Containment 576 References 578 Chapter 21 Antimicrobial Resistance 585 Introduction 585 Global Scenario 586 Drug-Resistant Organisms 588 Causes of Drug Resistance 593 Mechanisms of Drug Resistance 595 Host Factors 598 Health-Related and Economic Hazards 599 Laboratory Diagnosis 601 Managing Antimicrobial Resistance 607 Prevention and Control 610 References 612 Chapter 22 Conventional Methods for Mosquito Control 615 Mosquito: Habits and Attractants 615 Environmental Management 618 Antilarval Measures 623 Chemical Adulticides 627 Repellents 630 Insecticide-Impregnated Bed Nets and Screens 632 References 633 Chapter 23 New and Potential Techniques: Mosquito Control 635 Myco-Insecticides 635 Entomopathogenic Bacteria and Viruses 637 Hormonomimetic and Plant-Derived Substances 639 Larvivorous Fish and Crustaceans 640 Dragonfly Nymphs 643 Protozoa 643 Mermithid Nematodes 644 Predator Larvae 645 Genetic Engineering 646 References 648 Chapter 24 Other Disease Vectors and Their Control 651 Housefly 651 Sand Fly 653 Deer Fly 653 Black Fly 655 Tsetse Fly 655 Water Flea (Cyclops) 656 Sand Flea (Jigger or Chigoe Flea) 657 Rat Flea 658 Reduviid Bug 658 Ticks (Hard and Soft) 659 Lice 662 Cockroach 663 Mites (Chiggers) 664 General Principles of Vector Control 666 Integrated Vector Management 667 Rodents 670 Methods for Rodent Control 671 References 672 Glossary 674 Index 683

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Book SynopsisThis book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing datTrade Review“In summary, the book is a must-have tool for any biostatistician dealing with missing data. It is an excellent reference book for postgraduate students or researchers working in the area of missing data.” (Biometrical Journal, 1 June 2015) “This is an excellent addition to the field, dealing with problems arising from missing data or unobserved data in clinical trials. It successfully bridges the gap between clinicians and statisticians using relatively common language to build common ground.” (Doody’s, 9 January 2015)Table of ContentsPreface xv References xvii Acknowledgments xix Notation xxi Table of SAS code fragments xxv Contributors xxix 1 What’s the problem with missing data? 1Michael O’Kelly and Bohdana Ratitch 1.1 What do we mean by missing data? 2 1.1.1 Monotone and non-monotone missing data 3 1.1.2 Modeling missingness, modeling the missing value and ignorability 4 1.1.3 Types of missingness (MCAR, MAR and MNAR) 4 1.1.4 Missing data and study objectives 5 1.2 An illustration 6 1.3 Why can’t I use only the available primary endpoint data? 7 1.4 What’s the problem with using last observation carried forward? 9 1.5 Can we just assume that data are missing at random? 11 1.6 What can be done if data may be missing not at random? 14 1.7 Stress-testing study results for robustness to missing data 15 1.8 How the pattern of dropouts can bias the outcome 15 1.9 How do we formulate a strategy for missing data? 16 1.10 Description of example datasets 18 1.10.1 Example dataset in Parkinson’s disease treatment 18 1.10.2 Example dataset in insomnia treatment 23 1.10.3 Example dataset in mania treatment 28 Appendix 1.A: Formal definitions of MCAR, MAR and MNAR 33 References 34 2 The prevention of missing data 36Sara Hughes 2.1 Introduction 36 2.2 The impact of “too much” missing data 37 2.2.1 Example from human immunodeficiency virus 38 2.2.2 Example from acute coronary syndrome 38 2.2.3 Example from studies in pain 39 2.3 The role of the statistician in the prevention of missing data 39 2.3.1 Illustrative example from HIV 41 2.4 Methods for increasing subject retention 48 2.5 Improving understanding of reasons for subject withdrawal 49 Acknowledgments 49 Appendix 2.A: Example protocol text for missing data prevention 49 References 50 3 Regulatory guidance – a quick tour 53Michael O’Kelly 3.1 International conference on harmonization guideline: Statistical principles for clinical trials: E9 54 3.2 The US and EU regulatory documents 55 3.3 Key points in the regulatory documents on missing data 55 3.4 Regulatory guidance on particular statistical approaches 57 3.4.1 Available cases 57 3.4.2 Single imputation methods 57 3.4.3 Methods that generally assume MAR 59 3.4.4 Methods that are used assuming MNAR 60 3.5 Guidance about how to plan for missing data in a study 62 3.6 Differences in emphasis between the NRC report and EU guidance documents 63 3.6.1 The term “conservative” 63 3.6.2 Last observation carried forward 63 3.6.3 Post hoc analyses 63 3.6.4 Non-monotone or intermittently missing data 63 3.6.5 Assumptions should be readily interpretable 65 3.6.6 Study report 65 3.6.7 Training 65 3.7 Other technical points from the NRC report 66 3.7.1 Time-to-event analyses 66 3.7.2 Tipping point sensitivity analyses 66 3.8 Other US/EU/international guidance documents that refer to missing data 66 3.8.1 Committee for medicinal products for human use guideline on anti-cancer products, recommendations on survival analysis 66 3.8.2 US guidance on considerations when research supported by office of human research protections is discontinued 67 3.8.3 FDA guidance on data retention 67 3.9 And in practice? 67 References 69 4 A guide to planning for missing data 71Michael O’Kelly and Bohdana Ratitch 4.1 Introduction 72 4.1.1 Missing data may bias trial results or make them more difficult to generalize to subjects outside the trial 72 4.1.2 Credibility of trial results when there is missing data 74 4.1.3 Demand for better practice with regard to missing data 74 4.2 Planning for missing data 76 4.2.1 The case report form and non-statistical sections of the protocol 76 4.2.2 The statistical sections of the protocol and the statistical analysis plan 81 4.2.3 Using historic data to narrow the choice of primary analysis and sensitivity analyses 88 4.2.4 Key points in choosing an approach for missing data 108 4.3 Exploring and presenting missingness 113 4.4 Model checking 114 4.5 Interpreting model results when there is missing data 116 4.6 Sample size and missing data 117 Appendix 4.A: Sample protocol/SAP text for study in Parkinson’s disease 119 Appendix 4.B: A formal definition of a sensitivity parameter 125 References 126 5 Mixed models for repeated measures using categorical time effects (MMRM) 130Sonia Davis 5.1 Introduction 131 5.2 Specifying the mixed model for repeated measures 132 5.2.1 The mixed model 132 5.2.2 Covariance structures 135 5.2.3 Mixed model for repeated measures versus generalized estimating equations 139 5.2.4 Mixed model for repeated measures versus last observation carried forward 140 5.3 Understanding the data 141 5.3.1 Parkinson’s disease example 141 5.3.2 A second example showing the usefulness of plots: The CATIE study 144 5.4 Applying the mixed model for repeated measures 145 5.4.1 Specifying the model 146 5.4.2 Interpreting and presenting results 150 5.5 Additional mixed model for repeated measures topics 162 5.5.1 Treatment by subgroup and treatment by site interactions 162 5.5.2 Calculating the effect size 164 5.5.3 Another strategy to model baseline 166 5.6 Logistic regression mixed model for repeated measures using the generalized linear mixed model 168 5.6.1 The generalized linear mixed model 168 5.6.2 Specifying the model 170 5.6.3 Interpreting and presenting results 173 5.6.4 Other modeling options 181 References 182 Table of SAS Code Fragments 183 6 Multiple imputation 185Bohdana Ratitch 6.1 Introduction 185 6.1.1 How is multiple imputation different from single imputation? 186 6.1.2 How is multiple imputation different from maximum likelihood methods? 187 6.1.3 Multiple imputation’s assumptions about missingness mechanism 188 6.1.4 A general three-step process for multiple imputation and inference 189 6.1.5 Imputation versus analysis model 190 6.1.6 Note on notation use 192 6.2 Imputation phase 192 6.2.1 Missing patterns: Monotone and non-monotone 192 6.2.2 How do we get multiple imputations? 195 6.2.3 Imputation strategies: Sequential univariate versus joint multivariate 197 6.2.4 Overview of the imputation methods 199 6.2.5 Reusing the multiply-imputed dataset for different analyses or summary scales 212 6.3 Analysis phase: Analyzing multiple imputed datasets 213 6.4 Pooling phase: Combining results from multiple datasets 216 6.4.1 Combination rules 216 6.4.2 Pooling analyses of continuous outcomes 219 6.4.3 Pooling analyses of categorical outcomes 222 6.5 Required number of imputations 227 6.6 Some practical considerations 231 6.6.1 Choosing an imputation model 231 6.6.2 Multivariate normality 235 6.6.3 Rounding and restricting the range for the imputed values 238 6.6.4 Convergence of Markov chain Monte Carlo 240 6.7 Pre-specifying details of analysis with multiple imputation 244 Appendix 6.A: Additional methods for multiple imputation 245 References 251 Table of SAS Code Fragments 255 7 Analyses under missing-not-at-random assumptions 257Michael O’Kelly and Bohdana Ratitch 7.1 Introduction 258 7.2 Background to sensitivity analyses and pattern-mixture models 259 7.2.1 The purpose of a sensitivity analysis 259 7.2.2 Pattern-mixture models as sensitivity analyses 261 7.3 Two methods of implementing sensitivity analyses via pattern-mixture models 264 7.3.1 A sequential method of implementing pattern-mixture models with multiple imputation 264 7.3.2 Providing stress-testing “what ifs” using pattern-mixture models 266 7.3.3 Two implementations of pattern-mixture models for sensitivity analyses 267 7.3.4 Characteristics and limitations of the sequential modeling method of implementing pattern-mixture models 268 7.3.5 Pattern-mixture models implemented using the joint modeling method 271 7.3.6 Characteristics of the joint modeling method of implementing pattern-mixture models 279 7.3.7 Summary of differences between the joint modelling and sequential modeling methods 281 7.4 A “toolkit”: Implementing sensitivity analyses via SAS 284 7.4.1 Reminder: General approach using multiple imputation with regression 284 7.4.2 Sensitivity analyses assuming withdrawals have trajectory of control arm 288 7.4.3 Sensitivity analyses assuming withdrawals have distribution of control arm 292 7.4.4 Baseline-observation-carried-forward-like and last-observation-carried-forward-like analyses 297 7.4.5 The general principle of using selected subsets of observed data as the basis to implement “what if” stress tests 306 7.4.6 Using a mixture of “what ifs,” depending on reason for discontinuation 306 7.4.7 Assuming trajectory of withdrawals is worse by some 𝛿: Delta adjustment and tipping point analysis 308 7.5 Examples of realistic strategies and results for illustrative datasets of three indications 320 7.5.1 Parkinson’s disease 320 7.5.2 Insomnia 323 7.5.3 Mania 330 Appendix 7.A How one could implement the neighboring case missing value assumption using visit-by-visit multiple imputation 335 Appendix 7.B SAS code to model withdrawals from the experimental arm, using observed data from the control arm 336 Appendix 7.C SAS code to model early withdrawals from the experimental arm, using the last-observation-carried-forward-like values 342 Appendix 7.D SAS macro to impose delta adjustment on a responder variable in the mania dataset 345 Appendix 7.E SAS code to implement tipping point via exhaustive scenarios for withdrawals in the mania dataset 346 Appendix 7.F SAS code to perform sensitivity analyses for the Parkinson’s disease dataset 348 Appendix 7.G SAS code to perform sensitivity analyses for the insomnia dataset 351 Appendix 7.H SAS code to perform sensitivity analyses for the mania dataset 356 Appendix 7.I Selection models 358 Appendix 7.J Shared parameter models 362 References 365 Table of SAS Code Fragments 368 8 Doubly robust estimation 369Belinda Hernandez, Ilya Lipkovich and Michael O’Kelly 8.1 Introduction 370 8.2 Inverse probability weighted estimation 370 8.2.1 Inverse probability weighting estimators for estimating equations 372 8.2.2 Summary of inverse probability weighting advantages 373 8.2.3 Inverse probability weighting disadvantages 373 8.3 Doubly robust estimation 374 8.3.1 Doubly robust methods explained 375 8.3.2 Advantages of doubly robust methods 376 8.3.3 Limitations of doubly robust methods 376 8.4 Vansteelandt et al. method for doubly robust estimation 377 8.4.1 Theoretical justification for the Vansteelandt et al. method 378 8.4.2 Implementation of the Vansteelandt et al. method for doubly robust estimation 379 8.5 Implementing the Vansteelandt et al. method via SAS 383 8.5.1 Mania dataset 383 8.5.2 Insomnia dataset 390 Appendix 8.A How to implement Vansteelandt et al. method for mania dataset (binary response) 392 Appendix 8.B SAS code to calculate estimates from the bootstrapped datasets 400 Appendix 8.C How to implement Vansteelandt et al. method for insomnia dataset 401 References 408 Table of SAS Code Fragments 408 Bibliography 409 Index 423

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Book SynopsisA comprehensive and practical overview of the identification, conduct and analysis of optimal Phase II trial design.Table of ContentsContributors xv Foreword I xvii Elizabeth A. Eisenhauer Foreword II xix Roger A’Hern Preface xxi 1 Introduction 1 Sarah Brown, Julia Brown, Walter Gregory and Chris Twelves 1.1 The role of phase II trials in cancer 3 1.2 The importance of appropriate phase II trial design 5 1.3 Current use of phase II designs 6 1.4 Identifying appropriate phase II trial designs 7 1.5 Potential trial designs 9 1.6 Using the guidance to design your trial 10 2 Key Points for Consideration 12 Sarah Brown, Julia Brown, Marc Buyse, Walter Gregory, Mahesh Parmar and Chris Twelves 2.1 Stage 1 – Trial questions 14 2.1.1 Therapeutic considerations 14 2.1.2 Primary intention of trial 16 2.1.3 Number of experimental treatment arms 17 2.1.4 Primary outcome of interest 18 2.2 Stage 2 – Design components 18 2.2.1 Outcome measure and distribution 18 2.2.2 Randomisation 21 2.2.3 Design category 26 2.3 Stage 3 – Practicalities 33 2.3.1 Practical considerations 33 2.4 Summary 35 3 Designs for Single Experimental Therapies with a Single Arm 36 Sarah Brown 3.1 One-stage designs 36 3.1.1 Binary outcome measure 36 3.1.2 Continuous outcome measure 38 3.1.3 Multinomial outcome measure 39 3.1.4 Time-to-event outcome measure 40 3.1.5 Ratio of times to progression 40 3.2 Two-stage designs 41 3.2.1 Binary outcome measure 41 3.2.2 Continuous outcome measure 50 3.2.3 Multinomial outcome measure 50 3.2.4 Time-to-event outcome measure 53 3.2.5 Ratio of times to progression 54 3.3 Multi-stage designs 55 3.3.1 Binary outcome measure 55 3.3.2 Continuous outcome measure 59 3.3.3 Multinomial outcome measure 59 3.3.4 Time-to-event outcome measure 60 3.3.5 Ratio of times to progression 60 3.4 Continuous monitoring designs 60 3.4.1 Binary outcome measure 60 3.4.2 Continuous outcome measure 63 3.4.3 Multinomial outcome measure 63 3.4.4 Time-to-event outcome measure 63 3.4.5 Ratio of times to progression 64 3.5 Decision-theoretic designs 64 3.5.1 Binary outcome measure 64 3.5.2 Continuous outcome measure 65 3.5.3 Multinomial outcome measure 65 3.5.4 Time-to-event outcome measure 65 3.5.5 Ratio of times to progression 65 3.6 Three-outcome designs 65 3.6.1 Binary outcome measure 65 3.6.2 Continuous outcome measure 66 3.6.3 Multinomial outcome measure 66 3.6.4 Time-to-event outcome measure 66 3.6.5 Ratio of times to progression 67 3.7 Phase II/III designs 67 4 Designs for Single Experimental Therapies Including Randomisation 68 Sarah Brown 4.1 One-stage designs 68 4.1.1 Binary outcome measure 68 4.1.2 Continuous outcome measure 70 4.1.3 Multinomial outcome measure 70 4.1.4 Time-to-event outcome measure 70 4.1.5 Ratio of times to progression 72 4.2 Two-stage designs 72 4.2.1 Binary outcome measure 72 4.2.2 Continuous outcome measure 73 4.2.3 Multinomial outcome measure 74 4.2.4 Time-to-event outcome measure 75 4.2.5 Ratio of times to progression 75 4.3 Multi-stage designs 75 4.3.1 Binary outcome measure 75 4.3.2 Continuous outcome measure 75 4.3.3 Multinomial outcome measure 75 4.3.4 Time-to-event outcome measure 76 4.3.5 Ratio of times to progression 76 4.4 Continuous monitoring designs 76 4.4.1 Binary outcome measure 76 4.4.2 Continuous outcome measure 76 4.4.3 Multinomial outcome measure 76 4.4.4 Time-to-event outcome measure 76 4.4.5 Ratio of times to progression 76 4.5 Three-outcome designs 77 4.5.1 Binary outcome measure 77 4.5.2 Continuous outcome measure 77 4.5.3 Multinomial outcome measure 77 4.5.4 Time-to-event outcome measure 77 4.5.5 Ratio of times to progression 77 4.6 Phase II/III designs 77 4.6.1 Binary outcome measure 77 4.6.2 Continuous outcome measure 79 4.6.3 Multinomial outcome measure 80 4.6.4 Time-to-event outcome measure 81 4.6.5 Ratio of times to progression 81 4.7 Randomised discontinuation designs 82 4.7.1 Binary outcome measure 82 4.7.2 Continuous outcome measure 82 4.7.3 Multinomial outcome measure 82 4.7.4 Time-to-event outcome measure 82 4.7.5 Ratio of times to progression 82 5 Treatment Selection Designs 83 Sarah Brown 5.1 Including a control arm 84 5.1.1 One-stage designs 84 5.1.2 Two-stage designs 84 5.1.3 Multi-stage designs 88 5.1.4 Continuous monitoring designs 89 5.1.5 Decision-theoretic designs 89 5.1.6 Three-outcome designs 89 5.1.7 Phase II/III designs – same primary outcome measure at phase II and phase III 89 5.1.8 Phase II/III designs – different primary outcome measures at phase II and phase III 99 5.1.9 Randomised discontinuation designs 102 5.2 Not including a control arm 103 5.2.1 One-stage designs 103 5.2.2 Two-stage designs 106 5.2.3 Multi-stage designs 108 5.2.4 Continuous monitoring designs 109 5.2.5 Decision-theoretic designs 110 5.2.6 Three-outcome designs 110 5.2.7 Phase II/III designs – same primary outcome measure at phase II and phase III 110 5.2.8 Randomised discontinuation designs 111 6 Designs Incorporating Toxicity as a Primary Outcome 112 Sarah Brown 6.1 Including a control arm 112 6.1.1 One-stage designs 112 6.1.2 Two-stage designs 114 6.1.3 Multi-stage designs 115 6.2 Not including a control arm 117 6.2.1 One-stage designs 117 6.2.2 Two-stage designs 118 6.2.3 Multi-stage designs 122 6.2.4 Continuous monitoring designs 125 6.3 Toxicity alone 126 6.3.1 One stage 126 6.3.2 Continuous monitoring 127 6.4 Treatment selection based on activity and toxicity 128 6.4.1 Two-stage designs 128 6.4.2 Multi-stage designs 129 6.4.3 Continuous monitoring designs 129 7 Designs Evaluating Targeted Subgroups 131 Sarah Brown 7.1 One-stage designs 131 7.1.1 Binary outcome measure 131 7.2 Two-stage designs 132 7.2.1 Binary outcome measure 132 7.3 Multi-stage designs 135 7.3.1 Binary outcome measure 135 7.3.2 Time-to-event outcome measure 137 7.4 Continuous monitoring designs 138 7.4.1 Binary outcome measure 138 7.4.2 Time-to-event outcome measure 139 8 ‘Chemo-radio-sensitisation’ in Head and Neck Cancer 141 John Chester and Sarah Brown Stage 1 – Trial questions 141 Therapeutic considerations 141 Primary intention of trial 142 Number of experimental treatment arms 142 Primary outcome of interest 142 Stage 2 – Design components 142 Outcome measure and distribution 142 Randomisation 143 Design category 143 Possible designs 144 Stage 3 – Practicalities 146 Practical considerations for selecting between designs 146 Proposed trial design 148 Summary 150 9 Combination Chemotherapy in Second-line Treatment of Non-small Cell Lung Cancer 151 Ornella Belvedere and Sarah Brown Stage 1 – Trial questions 152 Therapeutic considerations 152 Primary intention of trial 152 Number of experimental treatment arms 152 Primary outcome of interest 152 Stage 2 – Design components 153 Outcome measure and distribution 153 Randomisation 153 Design category 153 Possible designs 154 Stage 3 – Practicalities 155 Practical considerations for selecting between designs 155 Proposed trial design 158 Summary 162 10 Selection by Biomarker in Prostate Cancer 163 Rick Kaplan and Sarah Brown Stage 1 – Trial questions 164 Therapeutic considerations 164 Primary intention of trial 164 Number of experimental treatment arms 164 Primary outcome of interest 164 Stage 2 – Design components 165 Outcome measure and distribution 165 Randomisation 165 Design category 166 Possible designs 167 Stage 3 – Practicalities 168 Practical considerations for selecting between designs 168 Proposed trial design 170 Summary 171 11 Dose Selection in Advanced Multiple Myeloma 174 Sarah Brown and Steve Schey Stage 1 – Trial questions 174 Therapeutic considerations 174 Primary intention of trial 175 Number of experimental arms 175 Primary outcome of interest 175 Stage 2 – Design components 176 Outcome measure and distribution 176 Randomisation 176 Design category 177 Possible designs 177 Stage 3 – Practicalities 178 Practical considerations for selecting between designs 178 Proposed trial design 181 Summary 182 12 Targeted Therapy for Advanced Colorectal Cancer 185 Matthew Seymour and Sarah Brown Stage 1 – Trial questions 185 Therapeutic considerations 185 Primary intention of trial 186 Number of experimental treatment arms 186 Primary outcome of interest 186 Stage 2 – Design components 187 Outcome measure and distribution 187 Randomisation 187 Design category 188 Possible designs 189 Stage 3 – Practicalities 190 Practical considerations for selecting between designs 190 Proposed trial design 191 Summary 194 13 Phase II Oncology Trials: Perspective from Industry 195 Anthony Rossini, Steven Green and William Mietlowski 13.1 Introduction 195 13.2 Commercial challenges, drivers and considerations 196 13.3 Selecting designs by strategy 197 13.3.1 Basic strategies addressed by phase II studies 198 13.3.2 Potential registration 198 13.3.3 Exploratory activity 203 13.3.4 Regimen selection 204 13.3.5 Phase II to Support Predicting Success in Phase IIi 206 13.3.6 Phase II safety trials 208 13.3.7 Prospective identification of target populations 209 13.4 Discussion 210 References 213 Index 227

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Book SynopsisAn introduction to state-of-the-art modeling and simulation approaches for social and economic determinants of population health New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a comprehensive introduction to modeling and simulation that addresses the many complex research questions in social epidemiology and public health. This book highlights a variety of practical applications and illustrative examples with a focus on modeling and simulation approaches for the social and economic determinants of population health. The book contains classic case examples in agent-based modeling (ABM) as well as essential information on ABM applications to public health including for infectious disease modeling, obesity, and tobacco control. This book also surveys applications of microsimulation (MSM) including of tax-benefit policies to project impacts of the social determinants of health. Specifically, this book: Table of ContentsSection I: Introduction Chapter 1: A Primer on the Social Determinants of Health (Daniel Kim) Chapter 2: Rationale for New Modeling and Simulation Tools: Agent-Based Modeling and Microsimulation (Daniel Kim, Ross A. Hammond) Section II: Agent-Based Modeling Chapter 3: Overview of Current Concepts and Process for Agent-Based Modeling (Ross A. Hammond) Chapter 4: Agent-Based Modeling in the Social Sciences (Joseph T. Ornstein and Ross A. Hammond) Chapter 5: Agent-Based Modeling in Public Health (Joseph T. Ornstein and Ross A. Hammond) Chapter 6: Section Summary (Ross A. Hammond) Section III: Microsimulation Modeling Chapter 7: Concepts and Methods for Microsimulation Modeling in the Social Sciences (Gerlinde Verbist and Hilde Philips) Chapter 8: Empirical Evidence Using Microsimulation Models in the Social Sciences (Francesco Figari and Emanuela Lezzi) Chapter 9: Applications of Microsimulation Models to the Social Determinants of Health and Public Health: A Systematic Review of the Literature (Daniel Kim) Chapter 10: Section Summary (Daniel Kim) Section IV: Conclusions Chapter 11: Future Directions (Daniel Kim, Ross A. Hammond)

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Book SynopsisFeaturing a multidisciplinary approach with practical examples from biology, gerontology, and demography, The Biostatistics of Aging provides a statistical theoretical framework for aging and aging-related diseases in addition to genetic and environmental contributions to mortality.Table of ContentsPREFACE AND ACKNOWLEDGMENT ix 1 Introduction 1 2 An Account of Gompertzian Mortality through Statistical and Evolutionary Arguments 6 2.1 The Statistical Theory of Extreme Values 10 2.2 The Evolutionary Theory of Aging 36 3 The Argument against Gompertzian Mortality 69 3.1 Departures from the Gompertz Model 70 3.2 An Evolution-Based Model of Causation 72 4 The Index of Aging-Relatedness 93 4.1 A Survival Mixture Model of the Gompertz and Weibull Distributions 94 4.2 Definition and Interpretation of the Index of Aging-Relatedness 97 4.3 The Survival Mixture Model and Competing Risks 103 4.4 Estimation of the Model Parameters 107 4.5 Illustrative Application: The Israeli Ischemic Heart Disease Study 109 4.6 Precision of Estimation 122 5 Discussion: Implications 128 5.1 The Meaning of the Gompertz Parameter 128 5.2 Age as a Risk Factor for Disease 132 5.3 Are Aging-Related Diseases an Integral Part of Aging? 134 5.4 Biological versus Chronological Aging 135 5.5 The Public Health Notion of Compression of Morbidity 138 5.6 A Picture of Aging for the Twenty-First Century 143 APPENDIX A: PROOFS OF RESULTS IN SECTION 2.1.2 WITH SOME EXTENSIONS 154 APPENDIX B: DERIVATION OF HAMILTON’S EQUATION FOR THE FORCE OF NATURAL SELECTION ON MORTALITY 170 APPENDIX C: SOME PROPERTIES OF THE GOMPERTZ AND WEIBULL DISTRIBUTIONS 174 APPENDIX D: FIRST AND SECOND PARTIAL DERIVATIVES OF THE MIXTURE LOG-LIKELIHOOD FUNCTION 178 APPENDIX E: EXPECTATION–CONDITIONAL MAXIMIZATION (ECM) ALGORITHM 183 APPENDIX F: R PROGRAM 190 REFERENCES 226 AUTHOR INDEX 245 SUBJECT INDEX 253

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Book SynopsisThis book focuses on the practical application of good clinical practice (GCP) fundamentals and provides insight into roles and responsibilities included in planning, executing, and analyzing clinical trials. The authors describe the design of quality into clinical trial planning and the application of regulatory, scientific, administrative, business, and ethical considerations. Describes the design of quality into the clinical trial planning Has end-of-chapter questions and answers to check learning and comprehension Includes charts that visually summarize the content and allow readers to cross-reference details in relevant chapters Offers a companion website containing supplemental training resources Table of ContentsPreface viii About the Authors xi About the Companion Website xii Part I Good Clinical Practice History 1 1 History 3P. Michael Dubinsky Part II Drug Development in the Regulatory Environment 11 2 Regulatory Environment 13P. Michael Dubinsky 3 GCP in Context 25P. Michael Dubinsky 4 The Intersection of GCP and Regulation 31P. Michael Dubinsky 5 Regulatory Affairs 39P. Michael Dubinsky Part III Good Clinical Practice 47 6 GCP Definition and Principles 49Karen A. Henry 7 Players Roles and Responsibilities Overview 59Karen A. Henry 8 IRB/IEC Roles and Responsibilities 67P. Michael Dubinsky 9 Investigator and Sponsor Roles and Responsibilities 73Karen A. Henry 10 The Research Volunteer 85Karen A. Henry 11 Regulatory Authority – Roles and Responsibilities 93P. Michael Dubinsky Part IV Individual Clinical Trial 101 12 Individual Clinical Trial Overview 103Karen A. Henry 13 Risk Assessment and Quality Management 129P. Michael Dubinsky 14 Trial Management; Start-up, On-Study, and Close-Out 135Karen A. Henry 15 Trial Resourcing and Outsourcing 173Karen A. Henry 16 The Investigator’s Brochure 183Karen A. Henry 17 The Investigational Product (Clinical Supplies) 201P. Michael Dubinsky 18 The Clinical Trial Protocol and Amendments 211Karen A. Henry 19 Informed Consent and Other Human Subject Protection 239Karen A. Henry 20 Data Collection and Data Management 265Karen A. Henry 21 Safety Monitoring and Reporting 285Karen A. Henry 22 Monitoring Overview 301Karen A. Henry 23 Investigator/Institution Selection 323Karen A. Henry 24 Investigator/Institution Initiation 343Karen A. Henry 25 Investigator/Institution Interim Monitoring 363Karen A. Henry 26 Investigator/Institution Close-out 381Karen A. Henry 27 Study Design and Data Analysis 401Karen A. Henry 28 The Clinical Study Report 415Karen A. Henry 29 Essential Documents 435Karen A. Henry Part V Quality in Clinical Trials 451 30 Quality Systems in Clinical Research 453P. Michael Dubinsky 31 Quality Responsibilities 463P. Michael Dubinsky 32 Standard Operating Procedures 475P. Michael Dubinsky 33 Quality Assurance Components 489P. Michael Dubinsky 34 Regulatory Authority Inspections 497P. Michael Dubinsky References for all Chapters 503 Index 509

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Book SynopsisProvides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS and Excel applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across variTable of ContentsPreface xv Acknowledgments xix 1 Introduction 1 Big Data Analysis 1 Visual Data Analysis 2 Importance of Statistics for the Social and Health Sciences and Medicine 3 Historical Notes: Early Use of Statistics 4 Approach of the Book 6 Cases from Current Research 7 Research Design 9 Focus on Interpretation 9 2 Descriptive Statistics: Central Tendency 13 What is the Whole Truth? Research Applications (Spuriousness) 13 Descriptive and Inferential Statistics 16 The Nature of Data: Scales of Measurement 16 Descriptive Statistics: Central Tendency 23 Using SPSS® and Excel to Understand Central Tendency 28 Distributions 35 Describing the Normal Distribution: Numerical Methods 37 Descriptive Statistics: Using Graphical Methods 41 Terms and Concepts 47 Data Lab and Examples (with Solutions) 49 Data Lab: Solutions 51 3 Descriptive Statistics: Variability 55 Range 55 Percentile 56 Scores Based on Percentiles 57 Using SPSS® and Excel to Identify Percentiles 57 Standard Deviation and Variance 60 Calculating the Variance and Standard Deviation 61 Population SD and Inferential SD 66 Obtaining SD from Excel and SPSS® 67 Terms and Concepts 70 Data Lab and Examples (with Solutions) 71 Data Lab: Solutions 73 4 The Normal Distribution 77 The Nature of the Normal Curve 77 The Standard Normal Score: Z Score 79 The Z Score Table of Values 80 Navigating the Z Score Distribution 81 Calculating Percentiles 83 Creating Rules for Locating Z Scores 84 Calculating Z Scores 87 Working with Raw Score Distributions 90 Using SPSS® to Create Z Scores and Percentiles 90 Using Excel to Create Z Scores 94 Using Excel and SPSS® for Distribution Descriptions 97 Terms and Concepts 99 Data Lab and Examples (with Solutions) 99 Data Lab: Solutions 101 5 Probability and the Z Distribution 105 The Nature of Probability 106 Elements of Probability 106 Combinations and Permutations 109 Conditional Probability: Using Bayes’ Theorem 111 Z Score Distribution and Probability 112 Using SPSS® and Excel to Transform Scores 117 Using the Attributes of the Normal Curve to Calculate Probability 119 “Exact” Probability 123 From Sample Values to Sample Distributions 126 Terms and Concepts 127 Data Lab and Examples (with Solutions) 128 Data Lab: Solutions 129 6 Research Design and Inferential Statistics 133 Research Design 133 Experiment 136 Non-Experimental or Post Facto Research Designs 140 Inferential Statistics 143 Z Test 154 The Hypothesis Test 154 Statistical Significance 156 Practical Significance: Effect Size 156 Z Test Elements 156 Using SPSS® and Excel for the Z Test 157 Terms and Concepts 158 Data Lab and Examples (with Solutions) 161 Data Lab: Solutions 162 7 The T Test for Single Samples 165 Introduction 166 Z Versus T: Making Accommodations 166 Research Design 167 Parameter Estimation 169 The T Test 173 The T Test: A Research Example 176 Interpreting the Results of the T Test for a Single Mean 180 The T Distribution 181 The Hypothesis Test for the Single Sample T Test 182 Type I and Type II Errors 183 Effect Size 187 Effect Size for the Single Sample T Test 187 Power Effect Size and Beta 188 One- and Two-Tailed Tests 189 Point and Interval Estimates 192 Using SPSS® and Excel with the Single Sample T Test 196 Terms and Concepts 201 Data Lab and Examples (with Solutions) 201 Data Lab: Solutions 203 8 Independent Sample T Test 207 A Lot of “Ts” 207 Research Design 208 Experimental Designs and the Independent T Test 208 Dependent Sample Designs 209 Between and Within Research Designs 210 Using Different T Tests 211 Independent T Test: The Procedure 213 Creating the Sampling Distribution of Differences 215 The Nature of the Sampling Distribution of Differences 216 Calculating the Estimated Standard Error of Difference with Equal Sample Size 218 Using Unequal Sample Sizes 219 The Independent T Ratio 221 Independent T Test Example 222 Hypothesis Test Elements for the Example 222 Before–After Convention with the Independent T Test 226 Confidence Intervals for the Independent T Test 227 Effect Size 228 The Assumptions for the Independent T Test 230 SPSS® Explore for Checking the Normal Distribution Assumption 231 Excel Procedures for Checking the Equal Variance Assumption 233 SPSS® Procedure for Checking the Equal Variance Assumption 237 Using SPSS® and Excel with the Independent T Test 239 SPSS® Procedures for the Independent T Test 239 Excel Procedures for the Independent T Test 243 Effect Size for the Independent T Test Example 245 Parting Comments 245 Nonparametric Statistics: The Mann–Whitney U Test 246 Terms and Concepts 249 Data Lab and Examples (with Solutions) 249 Data Lab: Solutions 251 Graphics in the Data Summary 254 9 Analysis of Variance 255 A Hypothetical Example of ANOVA 255 The Nature of ANOVA 257 The Components of Variance 258 The Process of ANOVA 259 Calculating ANOVA 260 Effect Size 268 Post Hoc Analyses 269 Assumptions of ANOVA 274 Additional Considerations with ANOVA 275 The Hypothesis Test: Interpreting ANOVA Results 276 Are the Assumptions Met? 276 Using SPSS® and Excel with One-Way ANOVA 282 The Need for Diagnostics 289 Non-Parametric ANOVA Tests: The Kruskal–Wallis Test 289 Terms and Concepts 292 Data Lab and Examples (with Solutions) 293 Data Lab: Solutions 294 10 Factorial ANOVA 297 Extensions of ANOVA 297 ANCOVA 298 MANOVA 299 MANCOVA 299 Factorial ANOVA 299 Interaction Effects 299 Simple Effects 301 2XANOVA: An Example 302 Calculating Factorial ANOVA 303 The Hypotheses Test: Interpreting Factorial ANOVA Results 306 Effect Size for 2XANOVA: Partial 𝜂2 308 Discussing the Results 309 Using SPSS® to Analyze 2XANOVA 311 Summary Chart for 2XANOVA Procedures 319 Terms and Concepts 319 Data Lab and Examples (with Solutions) 320 Data Lab: Solutions 320 11 Correlation 329 The Nature of Correlation 330 The Correlation Design 331 Pearson’s Correlation Coefficient 332 Plotting the Correlation: The Scattergram 334 Using SPSS® to Create Scattergrams 337 Using Excel to Create Scattergrams 339 Calculating Pearson’s r 341 The Z Score Method 342 The Computation Method 344 The Hypothesis Test for Pearson’s r 345 Effect Size: the Coefficient of Determination 347 Diagnostics: Correlation Problems 349 Correlation Using SPSS® and Excel 352 Nonparametric Statistics: Spearman’s Rank Order Correlation (rs) 358 Terms and Concepts 363 Data Lab and Examples (with Solutions) 364 Data Lab: Solutions 365 12 Bivariate Regression 371 The Nature of Regression 372 The Regression Line 374 Calculating Regression 376 Effect Size of Regression 379 The Z Score Formula for Regression 380 Testing the Regression Hypotheses 382 The Standard Error of Estimate 383 Confidence Interval 385 Explaining Variance Through Regression 386 A Numerical Example of Partitioning the Variation 389 Using Excel and SPSS® with Bivariate Regression 390 The SPSS® Regression Output 390 The Excel Regression Output 396 Complete Example of Bivariate Linear Regression 398 Assumptions of Bivariate Regression 398 The Omnibus Test Results 404 Effect Size 404 The Model Summary 405 The Regression Equation and Individual Predictor Test of Significance 405 Advanced Regression Procedures 406 Detecting Problems in Bivariate Linear Regression 408 Terms and Concepts 409 Data Lab and Examples (with Solutions) 410 Data Lab: Solutions 411 13 Introduction to Multiple Linear Regression 417 The Elements of Multiple Linear Regression 417 Same Process as Bivariate Regression 418 Some Differences between Bivariate Linear Regression and Multiple Linear Regression 419 Stuff not Covered 420 Assumptions of Multiple Linear Regression 421 Analyzing Residuals to Check MLR Assumptions 422 Diagnostics for MLR: Cleaning and Checking Data 423 Extreme Scores 424 Distance Statistics 428 Influence Statistics 429 MLR Extended Example Data 430 Assumptions Met? 431 Analyzing Residuals: Are Assumptions Met? 433 Interpreting the SPSS® Findings for MLR 436 Entering Predictors Together as a Block 437 Entering Predictors Separately 442 Additional Entry Methods for MLR Analyses 447 Example Study Conclusion 448 Terms and Concepts 448 Data Lab and Example (with Solution) 450 Data Lab: Solution 450 14 Chi-Square and Contingency Table Analysis 455 Contingency Tables 455 The Chi-square Procedure and Research Design 456 Chi-square Design One: Goodness of Fit 457 A Hypothetical Example: Goodness of Fit 458 Effect Size: Goodness of Fit 462 Chi-square Design Two: The Test of Independence 463 A Hypothetical Example: Test of Independence 464 Special 2 × 2 Chi-square 468 Effect Size in 2 × 2 Tables: PHI 470 Cramer’s V: Effect Size for the Chi-square Test of Independence 471 Repeated Measures Chi-square: Mcnemar Test 472 Using SPSS® and Excel with Chi-square 474 Using SPSS® for the Chi-square Test of Independence 475 Using Excel for Chi-square Analyses 481 Terms and Concepts 483 Data Lab and Examples (with Solutions) 483 Data Lab: Solutions 484 15 Repeated Measures Procedures: Tdep and ANOVAWS 489 Independent and Dependent Samples in Research Designs 490 Using Different T Tests 491 The Dependent T Test Calculation: The “Long” Formula 491 Example: The Long Formula 492 The Dependent T Test Calculation: The “Difference” Formula 494 Tdep and Power 496 Conducting The Tdep Analysis Using SPSS® 496 Conducting The Tdep Analysis Using Excel 498 Within-Subject ANOVA (ANOVAWS) 498 Experimental Designs 499 Post Facto Designs 500 Within-Subject Example 501 Using SPSS® for Within-Subject Data 501 The SPSS® Procedure 502 The SPSS® Output 504 Nonparametric Statistics 508 Terms and Concepts 508 Appendices Appendix A SPSS® Basics 509 Using SPSS® 509 General Features 510 Management Functions 513 Additional Management Functions 517 Appendix B Excel Basics 531 Data Management 531 The Excel Menus 533 Using Statistical Functions 541 Data Analysis Procedures 543 Missing Values and “0” Values in Excel Analyses 544 Using Excel with “Real Data” 544 Appendix C Statistical Tables 545 Table C.1: Z-Score Table (Values Shown are Percentages – %) 545 Table C.2: Exclusion Values for the T-Distribution 547 Table C.3: Critical (Exclusion) Values for the Distribution of F 548 Table C.4: Tukey’s Range Test (Upper 5% Points) 551 Table C.5: Critical (Exclusion) Values for Pearson’s Correlation Coefficient r 552 Table C.6: Critical Values of the 𝜒2 (Chi-Square) Distribution 553 References 555 Index 557

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Book SynopsisThis book presents the methodology and applications of a range of important topics in statistics, and is designed for graduate students in Statistics and Biostatistics and for medical researchers. Illustrations and more than ninety exercises with solutions are presented.Table of ContentsTopics for illustrations, examples and exercises xv Preface xvii List of abbreviations xix 1 Statistical measures 1 1.1 Introduction 1 1.2 Mean, mode and median 2 1.3 Variance and standard deviation 3 1.4 Quartiles, deciles and percentiles 4 1.5 Skewness and kurtosis 5 1.6 Frequency distributions 6 1.7 Covariance and correlation 7 1.8 Joint frequency distribution 9 1.9 Linear transformation of the observations 10 1.10 Linear combinations of two sets of observations 10 Exercises 11 2 Probability, random variable, expected value and variance 14 2.1 Introduction 14 2.2 Events and probabilities 14 2.3 Mutually exclusive events 15 2.4 Independent and dependent events 15 2.5 Addition of probabilities 16 2.6 Bayes’ theorem 16 2.7 Random variables and probability distributions 17 2.8 Expected value, variance and standard deviation 17 2.9 Moments of a distribution 18 Exercises 18 3 Odds ratios, relative risk, sensitivity, specificity and the ROC curve 19 3.1 Introduction 19 3.2 Odds ratio 19 3.3 Relative risk 20 3.4 Sensitivity and specificity 21 3.5 The receiver operating characteristic (ROC) curve 22 Exercises 22 4 Probability distributions, expectations, variances and correlation 24 4.1 Introduction 24 4.2 Probability distribution of a discrete random variable 25 4.3 Discrete distributions 25 4.4 Continuous distributions 29 4.5 Joint distribution of two discrete random variables 34 4.6 Bivariate normal distribution 37 Exercises 38 5 Means, standard errors and confidence limits 40 5.1 Introduction 40 5.2 Expectation, variance and standard error (S.E.) of the sample mean 41 5.3 Estimation of the variance and standard error 42 5.4 Confidence limits for the mean 43 5.5 Estimator and confidence limits for the difference of two means 44 5.6 Approximate confidence limits for the difference of two means 46 5.7 Matched samples and paired comparisons 47 5.8 Confidence limits for the variance 48 5.9 Confidence limits for the ratio of two variances 49 5.10 Least squares and maximum likelihood methods of estimation 49 Exercises 51 6 Proportions, odds ratios and relative risks: Estimation and confidence limits 54 6.1 Introduction 54 6.2 A single proportion 54 6.3 Confidence limits for the proportion 55 6.4 Difference of two proportions or percentages 56 6.5 Combining proportions from independent samples 56 6.6 More than two classes or categories 57 6.7 Odds ratio 58 6.8 Relative risk 59 Exercises 59 7 Tests of hypotheses: Means and variances 62 7.1 Introduction 62 7.2 Principle steps for the tests of a hypothesis 63 7.3 Right-sided alternative, test statistic and critical region 65 7.4 Left-sided alternative and the critical region 69 7.5 Two-sided alternative, critical region and the p-value 72 7.6 Difference between two means: Variances known 75 7.7 Matched samples and paired comparison 77 7.8 Test for the variance 77 7.9 Test for the equality of two variances 78 7.10 Homogeneity of variances 79 Exercises 80 8 Tests of hypotheses: Proportions and percentages 82 8.1 A single proportion 82 8.2 Right-sided alternative 82 8.3 Left-sided alternative 85 8.4 Two-sided alternative 87 8.5 Difference of two proportions 90 8.6 Specified difference of two proportions 95 8.7 Equality of two or more proportions 95 8.8 A common proportion 96 Exercises 97 9 The Chisquare statistic 99 9.1 Introduction 99 9.2 The test statistic 99 9.3 Test of goodness of fit 101 9.4 Test of independence: (r x c) classification 101 9.5 Test of independence: (2x2) classification 104 Exercises 107 10 Regression and correlation 110 10.1 Introduction 110 10.2 The regression model: One independent variable 110 10.3 Regression on two independent variables 118 10.4 Multiple regression: The least squares estimation 124 10.5 Indicator variables 132 10.6 Regression through the origin 135 10.7 Estimation of trends 136 10.8 Logistic regression and the odds ratio 138 10.9 Weighted Least Squares (WLS) estimator 141 10.10 Correlation 142 10.11 Further topics in regression 144 Exercises 148 11 Analysis of variance and covariance: Designs of experiments 152 11.1 Introduction 152 11.2 One-way classification: Balanced design 153 11.3 One-way random effects model: Balanced design 155 11.4 Inference for the variance components and the mean 155 11.5 One-way classification: Unbalanced design and fixed effects 157 11.6 Unbalanced one-way classification: Random effects 159 11.7 Intraclass correlation 160 11.8 Analysis of covariance: The balanced design 161 11.9 Analysis of covariance: Unbalanced design 165 11.10 Randomized blocks 168 11.11 Repeated measures design 170 11.12 Latin squares 172 11.13 Cross-over design 174 11.14 Two-way cross-classification 175 11.15 Missing observations in the designs of experiments 184 Exercises 186 12 Meta-analysis 190 12.1 Introduction 190 12.2 Illustrations of large-scale studies 190 12.3 Fixed effects model for combining the estimates 191 12.4 Random effects model for combining the estimates 193 12.5 Alternative estimators for σ2 α 194 12.6 Tests of hypotheses and confidence limits for the variance components 194 Exercises 195 13 Survival analysis 197 13.1 Introduction 197 13.2 Survival and hazard functions 198 13.3 Kaplan-Meir product-limit estimator 198 13.4 Standard error of Ŝ(tm) and confidence limits for S(tm) 199 13.5 Confidence limits for S(tm) with the right-censored observations 199 13.6 Log-Rank test for the equality of two survival distributions 201 13.7 Cox’s proportional hazard model 202 Exercises 203 14 Nonparametric statistics 205 14.1 Introduction 205 14.2 Spearman’s rank correlation coefficient 205 14.3 The Sign test 206 14.4 Wilcoxon (1945) Matched-pairs Signed-ranks test 208 14.5 Wilcoxon’s test for the equality of the distributions of two non-normal populations with unpaired sample observations 209 14.6 McNemer’s (1955) matched pair test for two proportions 210 14.7 Cochran’s (1950) Q-test for the difference of three or more matched proportions 211 14.8 Kruskal-Wallis one-way ANOVA test by ranks 212 Exercises 213 15 Further topics 215 15.1 Introduction 215 15.2 Bonferroni inequality and the Joint Confidence Region 215 15.3 Least significant difference (LSD) for a pair of treatment effects 217 15.4 Tukey’s studentized range test 217 15.5 Scheffe’s simultaneous confidence intervals 218 15.6 Bootstrap confidence intervals 219 15.7 Transformations for the ANOVA 220 Exercises 221 Solutions to exercises 222 Appendix tables 249 References 261 Index 264

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Book SynopsisA hands-on guide to using statistics in health research, from planning, through analysis, and on to reporting A Practical Approach to Using Statistics in Health Research offers an easy to use, step-by-step guide for using statistics in health research. The authors use their experience of statistics and health research to explain how statistics fit in to all stages of the research process. They explain how to determine necessary sample sizes, interpret whether there are statistically significant difference in outcomes between groups, and use measured effect sizes to decide whether any changes are large enough to be relevant to professional practice. The text walks you through how to identify the main outcome measure for your study and the factor which you think may influence that outcome and then determine what type of data will be used to record both of these. It then describes how this information is used to select the most appropriate methods to report and analyze your data. A steTable of ContentsAbout the Companion Website xv 1 Introduction 1 1.1 At Whom is This Book Aimed? 1 1.2 At What Scale of Project is This Book Aimed? 2 1.3 Why Might This Book be Useful for You? 2 1.4 How to Use This Book 3 1.5 Computer Based Statistics Packages 4 1.6 Relevant Videos etc. 5 2 Data Types 7 2.1 What Types of Data are There and Why Does it Matter? 7 2.2 Continuous Measured Data 7 2.2.1 Continuous Measured Data – Normal and Non‐Normal Distribution 8 2.2.2 Transforming Non‐Normal Data 13 2.3 Ordinal Data 13 2.4 Categorical Data 14 2.5 Ambiguous Cases 14 2.5.1 A Continuously Varying Measure that has been Divided into a Small Number of Ranges 14 2.5.2 Composite Scores with a Wide Range of Possible Values 15 2.6 Relevant Videos etc. 15 3 Presenting and Summarizing Data 17 3.1 Continuous Measured Data 17 3.1.1 Normally Distributed Data – Using the Mean and Standard Deviation 18 3.1.2 Data With Outliers, e.g. Skewed Data – Using Quartiles and the Median 18 3.1.3 Polymodal Data – Using the Modes 20 3.2 Ordinal Data 21 3.2.1 Ordinal Scales With a Narrow Range of Possible Values 22 3.2.2 Ordinal Scales With a Wide Range of Possible Values 22 3.2.3 Dividing an Ordinal Scale Into a Small Number of Ranges (e.g. Satisfactory/Unsatisfactory or Poor/Acceptable/Good) 22 3.2.4 Summary for Ordinal Data 23 3.3 Categorical Data 23 3.4 Relevant Videos etc. 24 Appendix 1: An Example of the Insensitivity of the Median When Used to Describe Data from an Ordinal Scale With a Narrow Range of Possible Values 25 4 Choosing a Statistical Test 27 4.1 Identify the Factor and Outcome 27 4.2 Identify the Type of Data Used to Record the Relevant Factor 29 4.3 Statistical Methods Where the Factor is Categorical 30 4.3.1 Identify the Type of Data Used to Record the Outcome 30 4.3.2 Is Continuous Measured Outcome Data Normally Distributed or Can It Be Transformed to Normality? 30 4.3.3 Identify Whether Your Sets of Outcome Data Are Related or Independent 31 4.3.4 For the Factor, How Many Levels Are Being Studied? 32 4.3.5 Determine the Appropriate Statistical Method for Studies with a Categorical Factor 32 4.4 Correlation and Regression with a Measured Factor 34 4.4.1 What Type of Data Was Used to Record Your Factor and Outcome? 34 4.4.2 When Both the Factor and the Outcome Consist of Continuous Measured Values, Select Between Pearson and Spearman Correlation 34 4.5 Relevant Additional Material 38 5 Multiple Testing 39 5.1 What Is Multiple Testing and Why Does It Matter? 39 5.2 What Can We Do to Avoid an Excessive Risk of False Positives? 40 5.2.1 Use of Omnibus Tests 40 5.2.2 Distinguishing Between Primary and Secondary/ Exploratory Analyses 40 5.2.3 Bonferroni Correction 41 6 Common Issues and Pitfalls 43 6.1 Determining Equality of Standard Deviations 43 6.2 How Do I Know, in Advance, How Large My SD Will Be? 43 6.3 One‐Sided Versus Two‐Sided Testing 44 6.4 Pitfalls That Make Data Look More Meaningful Than It Really Is 45 6.4.1 Too Many Decimal Places 45 6.4.2 Percentages with Small Sample Sizes 47 6.5 Discussion of Statistically Significant Results 47 6.6 Discussion of Non‐Significant Results 50 6.7 Describing Effect Sizes with Non‐Parametric Tests 51 6.8 Confusing Association with a Cause and Effect Relationship 52 7 Contingency Chi‐Square Test 55 7.1 When Is the Test Appropriate? 55 7.2 An Example 55 7.3 Presenting the Data 57 7.3.1 Contingency Tables 57 7.3.2 Clustered or Stacked Bar Charts 57 7.4 Data Requirements 59 7.5 An Outline of the Test 59 7.6 Planning Sample Sizes 59 7.7 Carrying Out the Test 60 7.8 Special Issues 61 7.8.1 Yates Correction 61 7.8.2 Low Expected Frequencies – Fisher’s Exact Test 61 7.9 Describing the Effect Size 61 7.9.1 Absolute Risk Difference (ARD) 62 7.9.2 Number Needed to Treat (NNT) 63 7.9.3 Risk Ratio (RR) 63 7.9.4 Odds Ratio (OR) 64 7.9.5 Case: Control Studies 65 7.10 How to Report the Analysis 65 7.10.1 Methods 65 7.10.2 Results 66 7.10.3 Discussion 67 7.11 Confounding and Logistic Regression 67 7.11.1 Reporting the Detection of Confounding 68 7.12 Larger Tables 69 7.12.1 Collapsing Tables 69 7 12.2 Reducing Tables 70 7.13 Relevant Videos etc. 71 8 Independent Samples (Two‐Sample) T‐Test 73 8.1 When Is the Test Applied? 73 8.2 An Example 73 8.3 Presenting the Data 75 8.3.1 Numerically 75 8.3.2 Graphically 75 8.4 Data Requirements 75 8.4.1 Variables Required 75 8.4.2 Normal Distribution of the Outcome Variable Within the Two Samples 75 8.4.3 Equal Standard Deviations 78 8.4.4 Equal Sample Sizes 78 8.5 An Outline of the Test 78 8.6 Planning Sample Sizes 79 8.7 Carrying Out the Test 79 8.8 Describing the Effect Size 79 8.9 How to Describe the Test, the Statistical and Practical Significance of Your Findings in Your Report 80 8.9.1 Methods Section 80 8.9.2 Results Section 80 8.9.3 Discussion Section 81 8.10 Relevant Videos etc. 81 9 Mann–Whitney Test 83 9.1 When Is the Test Applied? 83 9.2 An Example 83 9.3 Presenting the Data 85 9.3.1 Numerically 85 9.3.2 Graphically 85 9.3.3 Divide the Outcomes into Low and High Ranges 85 9.4 Data Requirements 86 9.4.1 Variables Required 86 9.4.2 Normal Distributions and Equality of Standard Deviations 87 9.4.3 Equal Sample Sizes 87 9.5 An Outline of the Test 87 9.6 Statistical Significance 87 9.7 Planning Sample Sizes 87 9.8 Carrying Out the Test 88 9.9 Describing the Effect Size 88 9.10 How to Report the Test 89 9.10.1 Methods Section 89 9.10.2 Results Section 89 9.10.3 Discussion Section 90 9.11 Relevant Videos etc. 91 10 One‐Way Analysis of Variance (ANOVA) – Including Dunnett’s and Tukey’s Follow Up Tests 93 10.1 When Is the Test Applied? 93 10.2 An Example 93 10.3 Presenting the Data 94 10.3.1 Numerically 94 10.3.2 Graphically 94 10.4 Data Requirements 94 10.4.1 Variables Required 94 10.4.2 Normality of Distribution for the Outcome Variable Within the Three Samples 95 10.4.3 Standard Deviations 96 10.4.4 Sample Sizes 98 10.5 An Outline of the Test 98 10.6 Follow Up Tests 98 10.7 Planning Sample Sizes 99 10.8 Carrying Out the Test 100 10.9 Describing the Effect Size 101 10.10 How to Report the Test 101 10.10.1 Methods 101 10.10.2 Results Section 102 10.10.3 Discussion Section 102 10.11 Relevant Videos etc. 103 11 Kruskal–Wallis 105 11.1 When Is the Test Applied? 105 11.2 An Example 105 11.3 Presenting the Data 106 11.3.1 Numerically 106 11.3.2 Graphically 107 11.4 Data Requirements 109 11.4.1 Variables Required 109 11.4.2 Normal Distributions and Standard Deviations 109 11.4.3 Equal Sample Sizes 110 11.5 An Outline of the Test 110 11.6 Planning Sample Sizes 110 11.7 Carrying Out the Test 110 11.8 Describing the Effect Size 111 11.9 Determining Which Group Differs from Which Other 111 11.10 How to Report the Test 111 11.10.1 Methods Section 111 11.10.2 Results Section 112 11.10.3 Discussion Section 113 11.11 Relevant Videos etc. 114 12 McNemar’s Test 115 12.1 When Is the Test Applied? 115 12.2 An Example 115 12.3 Presenting the Data 116 12.4 Data Requirements 116 12.5 An Outline of the Test 118 12.6 Planning Sample Sizes 118 12.7 Carrying Out the Test 119 12.8 Describing the Effect Size 119 12.9 How to Report the Test 119 12.9.1 Methods Section 119 12.9.2 Results Section 120 12.9.3 Discussion Section 120 12.10 Relevant Videos etc. 121 13 Paired T‐Test 123 13.1 When Is the Test Applied? 123 13.2 An Example 125 13.3 Presenting the Data 125 13.3.1 Numerically 125 13.3.2 Graphically 125 13.4 Data Requirements 126 13.4.1 Variables Required 126 13.4.2 Normal Distribution of the Outcome Data 126 13.4.3 Equal Standard Deviations 128 13.4.4 Equal Sample Sizes 128 13.5 An Outline of the Test 128 13.6 Planning Sample Sizes 129 13.7 Carrying Out the Test 129 13.8 Describing the Effect Size 129 13.9 How to Report the Test 130 13.9.1 Methods Section 130 13.9.2 Results Section 130 13.9.3 Discussion Section 131 13.10 Relevant Videos etc. 131 14 Wilcoxon Signed Rank Test 133 14.1 When Is the Test Applied? 133 14.2 An Example 134 14.3 Presenting the Data 134 14.3.1 Numerically 134 14.3.2 Graphically 136 14.4 Data Requirements 136 14.4.1 Variables Required 136 14.4.2 Normal Distributions and Equal Standard Deviations 137 14.4.3 Equal Sample Sizes 137 14.5 An Outline of the Test 137 14.6 Planning Sample Sizes 138 14.7 Carrying Out the Test 139 14.8 Describing the Effect Size 139 14.9 How to Report the Test 140 14.9.1 Methods Section 140 14.9.2 Results Section 140 14.9.3 Discussion Section 141 14.10 Relevant Videos etc. 141 15 Repeated Measures Analysis of Variance 143 15.1 When Is the Test Applied? 143 15.2 An Example 144 15.3 Presenting the Data 144 15.3.1 Numerical Presentation of the Data 145 15.3.2 Graphical Presentation of the Data 145 15.4 Data Requirements 146 15.4.1 Variables Required 146 15.4.2 Normal Distribution of the Outcome Data 148 15.4.3 Equal Standard Deviations 148 15.4.4 Equal Sample Sizes 148 15.5 An Outline of the Test 148 15.6 Planning Sample Sizes 149 15.7 Carrying Out the Test 150 15.8 Describing the Effect Size 150 15.9 How to Report the Test 151 15.9.1 Methods Section 151 15.9.2 Results Section 151 15.9.3 Discussion Section 152 15.10 Relevant Videos etc. 153 16 Friedman Test 155 16.1 When Is the Test Applied? 155 16.2 An Example 157 16.3 Presenting the Data 157 16.3.1 Bar Charts of the Outcomes at Various Stages 157 16.3.2 Summarizing the Data via Medians or Means 157 16.3.3 Splitting the Data at Some Critical Point in the Scale 159 16.4 Data Requirements 160 16.4.1 Variables Required 160 16.4.2 Normal Distribution and Standard Deviations in the Outcome Data 160 16.4.3 Equal Sample Sizes 160 16.5 An Outline of the Test 160 16.6 Planning Sample Sizes 161 16.7 Follow Up Tests 161 16.8 Carrying Out the Tests 162 16.9 Describing the Effect Size 162 16.9.1 Median or Mean Values Among the Individual Changes 162 16.9.2 Split the Scale 162 16.10 How to Report the Test 162 16.10.1 Methods Section 162 16.10.2 Results Section 163 16.10.3 Discussion Section 164 16.11 Relevant Videos etc. 164 17 Pearson Correlation 165 17.1 Presenting the Data 165 17.2 Correlation Coefficient and Statistical Significance 166 17.3 Planning Sample Sizes 167 17.4 Effect Size and Practical Relevance 167 17.5 Regression 169 17.6 How to Report the Analysis 170 17.6.1 Methods 170 17.6.2 Results 170 17.6.3 Discussion 171 17.7 Relevant Videos etc. 171 18 Spearman Correlation 173 18.1 Presenting the Data 173 18.2 Testing for Evidence of Inappropriate Distributions 174 18.3 Rho and Statistical Significance 174 18.4 An Outline of the Significance Test 175 18.5 Planning Sample Sizes 175 18.6 Effect Size 176 18.7 Where Both Measures Are Ordinal 176 18.7.1 Educational Level and Willingness to Undertake Internet Research – An Example Where Both Measures Are Ordinal 176 18.7.2 Presenting the Data 177 18.7.3 Rho and Statistical Significance 177 18.7.4 Effect Size 178 18.8 How to Report Spearman Correlation Analyses 178 18.8.1 Methods 178 18.8.2 Results 179 18.8.3 Discussion 180 18.9 Relevant Videos etc. 180 19 Logistic Regression 181 19.1 Use of Logistic Regression with Categorical Outcomes 181 19.2 An Outline of the Significance Test 182 19.3 Planning Sample Sizes 182 19.4 Results of the Analysis 184 19.5 Describing the Effect Size 184 19.6 How to Report the Analysis 185 19.6.1 Methods 185 19.6.2 Results 186 19.6.3 Discussion 186 19.7 Relevant Videos etc. 187 20 Cronbach’s Alpha 189 20.1 Appropriate Situations for the Use of Cronbach’s Alpha 189 20.2 Inappropriate Uses of Alpha 190 20.3 Interpretation 190 20.4 Reverse Scoring 191 20.5 An Example 191 20.6 Performing and Interpreting the Analysis 192 20.7 How to Report Cronbach’s Alpha Analyses 193 20.7.1 Methods Section 193 20.7.2 Results 194 20.7.3 Discussion 194 20.7 Relevant Videos etc. 195 Glossary 197 Videos 209 Index 211

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Book SynopsisTextbook of Zoonoses Comprehensive resource covering the aetiology, epidemiology and transmission cycle, clinical symptoms, diagnosis, and prevention and control strategies of the important zoonoses. Zoonoses are the diseases which can spread from animals to humans. This book covers all important zoonoses that are prevalent in today's world. As a modern learning resource, it incorporates recent scientific developments and concepts to give readers a complete overview of each zoonoses. Written by three well-qualified authors in academia, sample topics covered within the book include: Bacterial, viral, parasitic, rickettsial, fungal, prion, and foodborne zoonosesAetiology and epidemiology of each zoonotic diseaseClinical symptoms and diagnosis in animals and humansTreatment options, plus prevention and control strategiesCDC classification of zoonotic agents and the WHO's list of neglected zoonoses' Written for undergraduate and postgraduate students studying veterinary public health and epidemiology, Textbook of Zoonoses is also a helpful resource for other veterinary and medical professionals interested in public health and epidemiology.Trade Review"Specifically written as a curriculum textbook for undergraduate and postgraduate students studying veterinary public health and epidemiology, "Textbook of Zoonoses" is also a helpful resource for other veterinary and medical professionals interested in public health and epidemiology...a critically important and unreservedly recommended addition to personal, professional, community, veterinary school, college, and university library "- Library Bookwatch, Mar 23, Midwest Book ReviewTable of ContentsForeword Preface Acknowledgements Introduction to Zoonoses Understanding concepts and terms related to Zoonoses SECTION 1: BACTERIAL ZOONOSES 1. Anthrax 2. Brucellosis 3. Cat-scratch disease 4. Glanders 5. Leptospirosis 6. Lyme disease (or Lyme borreliosis) 7. Plague 8. Q fever 9. Tularemia 10. Zoonotic Chlamydiosis 11. Zoonotic Tuberculosis 12. Other zoonoses a. Meliodiosis b. Tetanus c. Dog-bite transmitted bacterial pathogens d. Rat Bite Fever agents Bacterial foodborne pathogens (Bacillus cereus, Campylobacteriosis, Clostridium perfringens, Clostridium botulinum, Diarrhoeagenic Escherichia coli, Listeria monocytogenes, Salmonellosis, Staphylococcus aureus, Vibriosis and Yersiniosis) SECTION 2: VIRAL ZOONOSES Introduction 13. Crimean Congo Haemorrhagic Fever (CCHF) 14. Ebola Haemorrhagic Fever 15. Hantavirus disease 16. Influenza viruses 17. Japanese Encephalitis 18. Nipah 19. Rabies 20. Rift Valley Fever 21. West Nile Fever 22. Yellow Fever 23. Zoonotic Coronaviruses 24. Viral Haemorrhagic fevers (Arenaviruses, Bunyaviruses, Filoviruses and Flaviviruses) 25. Other Zoonotic Viruses of Public Health Importance (Eastern equine encephalomyelitis (EEE), Western equine encephalomyelitis (WEE), Venezuelan equine encephalomyelitis (VEE), Foot and mouth disease (FMD), Hendra virus (HeV), Herpes B Virus (Cercopithecine herpesvirus 1), La Crosse encephalitis virus (LACV), Lymphocytic choriomeningitis virus (LCMV), Monkeypox virus, Powassan virus (POWV), Saint Louis encephalitis virus (SLEV) 26. Foodborne viral zoonoses SECTION 3: PARASITIC ZOONOSES Introduction 27. Amoebiasis 28. Balantidiasis 29. Cryptosporidiosis 30. Cutaneous Larvae Migrans 31. Diphyllobothriasis 32. Echinococcosis 33. Giardiasis 34. Leishmaniasis 35. Sarcocystosis 36. Schistosomiasis 37. Taeniasis/Cystecercosis complex 38. Toxoplasmosis 39. Trichinellosis 40. Trypanosomiasis 41. Visceral Larvae Migrans 42. Other parasitic zoonoses of public health importance a. Angiostrongyliasis b. Anisakiasis c. Clonorchiasis d. Dracunculiasis e. Fasciolopsiasis f. Paragonimiasis g. Pentastomiasis h. Primary Amoebic Meningoencephalitis (PAM) SECTION 4: FUNGAL ZOONOSES Introduction 43. Aspergillosis 44. Blastomycosis 45. Coccidioidomycosis 46. Cryptococcosis 47. Dermatophytosis 48. Histoplasmosis 49. Mucormycoses 50. Sporotrichosis 51. Other important fungal zooonoses SECTION 5: RICKETTSIAL ZOONOSES Introduction A. Typhus group 1. Epidemic typhus 2. Endemic typhus B. Spotted fever group 1. Tick borne spotted fever a. Rocky Mountain spotted fever b. Other important tick-borne spotted fever rickettsioses 2. Flea-borne spotted fever 3. Mite-borne spotted fever C. Scrub typhus Diagnosis of rickettsioses SECTION 6: PRION DISEASES ANNEXURES 1. Important Global Health Days 2. List of important zoonoses related to farm animals and pets 3. CDC classification of bioterrorism agents References Credits and Sources/Acknowledgments Index

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