Epidemiology and Medical statistics Books

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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Book SynopsisTable of ContentsPreface xi List of Abbreviations xiii Notation xv 1 Introduction 1 1.1 Random Truncation 1 1.2 One-sided Truncation 2 1.2.1 Left-truncation 2 1.2.2 Right-truncation 2 1.2.3 Truncation vs. Censoring 3 1.3 Double Truncation 3 1.4 Real Data Examples 5 1.4.1 Childhood Cancer Data 5 1.4.2 AIDS Blood Transfusion Data 6 1.4.3 Equipment-S Rounded Failure Time Data 7 1.4.4 Quasar Data 7 1.4.5 Parkinson’s Disease Data 8 1.4.6 Acute Coronary Syndrome Data 9 References 10 2 One-Sample Problems 13 2.1 Nonparametric Estimation of a Distribution Function 13 2.1.1 The NPMLE 14 2.1.2 Numerical Algorithms for Computing the NPMLE 21 2.1.3 Theoretical Properties of the NPMLE 24 2.1.4 Standard Errors and Confidence Limits 36 2.2 Semiparametric and Parametric Approaches 43 2.2.1 Semiparametric Approach 44 2.2.2 Parametric Approach 52 2.3 R Code for the Examples 56 2.3.1 Code for Example 2.1.8 56 2.3.2 Code for Examples 2.1.11 and 2.1.13 56 2.3.3 Code for Example 2.1.14 58 2.3.4 Code for Example 2.1.15 59 2.3.5 Code for Example 2.1.22 60 2.3.6 Code for Example 2.2.6 61 2.3.7 Code for Example 2.2.8 62 References 65 3 Smoothing Methods 69 3.1 Some Background in Kernel Estimation 69 3.2 Estimating the Density Function 71 3.3 Asymptotic Properties 71 3.4 Data-driven Bandwidth Selection 77 3.4.1 Normal Reference Bandwidth Selection 78 3.4.2 Plug-in Bandwidth Selection 79 3.4.3 Least-squares Cross-validation Bandwidth Selection 80 3.4.4 Smoothed Bootstrap Bandwidth Selection 81 3.4.5 Bandwidth Selectors in Practice 82 3.5 Further Issues in Kernel Density Estimation 88 3.6 Estimating the Hazard Function 90 3.7 R Code for the Examples 98 3.7.1 Code for Example 3.2.1 98 3.7.2 Code for Examples 3.3.4 and 3.3.5 99 3.7.3 Code for Examples 3.4.2 and 3.4.3 100 3.7.4 Code for Example 3.5.1 102 3.7.5 Code for Example 3.6.4 104 3.7.6 Code for Example 3.6.5 105 References 106 4 Regression Analysis 109 4.1 Observational Bias in Regression 109 4.2 Proportional Hazards Regression 114 4.3 Accelerated Failure Time Regression 117 4.4 Nonparametric Regression 121 4.5 R Code for the Examples 126 4.5.1 Code for Example 4.1.1 126 4.5.2 Code for Example 4.1.4 126 4.5.3 Code for Example 4.2.4 127 4.5.4 Code for Example 4.3.2 127 4.5.5 Code for Example 4.4.2 128 References 129 5 Further Topics 131 5.1 Two-Sample Problems 132 5.2 Competing Risks 137 5.2.1 Cumulative Incidences 139 5.2.2 Regression Models for Competing Risks 142 5.3 Testing for Quasi-independence 146 5.4 Dependent Truncation 150 5.5 R Code for the Examples 157 5.5.1 Code for Example 5.1.3 157 5.5.2 Code for Example 5.2.4 159 5.5.3 Code for Example 5.2.6 160 5.5.4 Code for Example 5.3.1 161 5.5.5 Code for Example 5.4.3 161 References 162 A Packages and Functions in R 165 A.1 Computing the NPMLE and Standard Errors 166 A.2 Assessing the Existence and Uniqueness of the NPMLE 167 A.3 Semiparametric and Parametric Estimation 168 A.4 Kernel Estimation 168 A.5 Regression Analysis 169 A.6 Competing Risks 169 A.7 Simulating Data 170 A.8 Testing Quasi-independence 170 A.9 Dependent Truncation 170 References 171 Index 173

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Book SynopsisA much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in healthcare research.Trade Review“Overall, the reviewers are enthusiastic about the book. The authors have covered all important areas of cRCTs, using a practical and pragmatic approach to the topic. The code is helpful for the practical implementation of the examples. The material is simple to understand, which will appeal to applied researchers, not only to biostatisticians. As such, we clearly recommend this book to all researchers interested in cRCTs. For biostatisticians involved in cRCTs and investigators of cRCTs, it is a must-have on the bookshelf.” (Biometrical Journal, 1 May 2015)Table of ContentsPreface xiii Acronyms and abbreviations xv 1 Introduction 1 1.1 Randomised controlled trials 1 1.1.1 A-Allocation at random 1 1.1.2 B-Blindness 2 1.1.3 C-Control 2 1.2 Complex interventions 3 1.3 History of cluster randomised trials 4 1.4 Cohort and field trials 4 1.5 The field/community trial 5 1.5.1 The REACT trial 5 1.5.2 The Informed Choice leaflets trial 6 1.5.3 The Mwanza trial 7 1.5.4 The paramedics practitioner trial 7 1.6 The cohort trial 8 1.6.1 The PoNDER trial 8 1.6.2 The DESMOND trial 9 1.6.3 The Diabetes Care from Diagnosis trial 10 1.6.4 The REPOSE trial 11 1.6.5 Other examples of cohort cluster trials 11 1.7 Field versus cohort designs 11 1.8 Reasons for cluster trials 12 1.9 Between- and within-cluster variation 14 1.10 Random-effects models for continuous outcomes 15 1.10.1 The model 15 1.10.2 The intracluster correlation coefficient 16 1.10.3 Estimating the intracluster correlation (ICC) coefficient 16 1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient 17 1.11 Random-effects models for binary outcomes 18 1.11.1 The model 18 1.11.2 The ICC for binary data 19 1.11.3 The coefficient of variation 19 1.11.4 Relationship between cvc and 𝜌 for binary data 20 1.12 The design effect 20 1.13 Commonly asked questions 21 1.14 Websources 21 Exercise 22 Appendix 1.A 22 2 Design issues 27 2.1 Introduction 27 2.2 Issues for a simple intervention 28 2.2.1 Phases of a trial 28 2.2.2 ‘Pragmatic’ and ‘explanatory’ trials 29 2.2.3 Intention-to-treat and per-protocol analyses 29 2.2.4 Non-inferiority and equivalence trials 30 2.3 Complex interventions 30 2.3.1 Design of complex interventions 30 2.3.2 Phase I modelling/qualitative designs 32 2.3.3 Pilot or feasibility studies 33 2.3.4 Example of pilot/feasibility studies in cluster trials 33 2.4 Recruitment bias 34 2.5 Matched-pair trials 34 2.5.1 Design of matched-pair studies 34 2.5.2 Limitations of matched-pairs designs 36 2.5.3 Example of matched-pair design: The Family Heart Study 36 2.6 Other types of designs 37 2.6.1 Cluster factorial designs 37 2.6.2 Example cluster factorial trial 38 2.6.3 Cluster crossover trials 38 2.6.4 Example of a cluster crossover trial 39 2.6.5 Stepped wedge 39 2.6.6 Pseudorandomised trials 40 2.7 Other design issues 41 2.8 Strategies for improving precision 41 2.9 Randomisation 42 2.9.1 Reasons for randomisation 42 2.9.2 Simple randomisation 43 2.9.3 Stratified randomisation 43 2.9.4 Restricted randomisation 43 2.9.5 Minimisation 44 Exercise 45 Appendix 2.A 48 3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial? 50 3.1 Introduction 51 3.1.1 Justification of the requirement for a sample size 51 3.1.2 Significance tests, P-values and power 51 3.1.3 Sample size and cluster trials 53 3.2 Sample size for continuous data – comparing two means 53 3.2.1 Basic formulae 53 3.2.2 The design effect (DE) in cluster RCTs 54 3.2.3 Example from general practice 55 3.3 Sample size for binary data – comparing two proportions 56 3.3.1 Sample size formula 56 3.3.2 Example calculations 57 3.3.3 Example: The Informed Choice leaflets study 58 3.4 Sample size for ordered categorical (ordinal) data 59 3.4.1 Sample size formula 59 3.4.2 Example calculations 60 3.5 Sample size for rates 62 3.5.1 Formulae 62 3.5.2 Example comparing rates 63 3.6 Sample size for survival 63 3.6.1 Formulae 63 3.6.2 Example of sample size for survival 64 3.7 Equivalence/non-inferiority studies 64 3.7.1 Equivalence/non-inferiority versus superiority 64 3.7.2 Continuous data – comparing the equivalence of two means 65 3.7.3 Example calculations for continuous data 65 3.7.4 Binary data – comparing the equivalence of two proportions 66 3.8 Unknown standard deviation and effect size 66 3.9 Practical problems 67 3.9.1 Tips on getting the SD 67 3.9.2 Non-response 67 3.9.3 Unequal groups 67 3.10 Number of clusters fixed 68 3.10.1 Number of clusters and number of subjects per cluster 68 3.10.2 Example with number of clusters fixed 69 3.10.3 Increasing the number of clusters or number of patients per cluster? 69 3.11 Values of the ICC 69 3.12 Allowing for imprecision in the ICC 70 3.13 Allowing for varying cluster sizes 70 3.13.1 Formulae 70 3.13.2 Example of effect of variable cluster size 71 3.14 Sample size re-estimation 71 3.14.1 Adjusting for covariates 72 3.15 Matched-pair studies 72 3.15.1 Sample sizes for matched designs 72 3.15.2 Example of a sample size calculation for a matched study 72 3.16 Multiple outcomes/endpoints 73 3.17 Three or more groups 74 3.18 Crossover trials 74 3.18.1 Formulae 75 3.18.2 Example of a sample size formula in a crossover trial 75 3.19 Post hoc sample size calculations 75 3.20 Conclusion: Usefulness of sample size calculations 76 3.21 Commonly asked questions 76 Exercise 77 Appendix 3.A 78 4 Simple analysis of cRCT outcomes using aggregate cluster-level summaries 83 4.1 Introduction 83 4.1.1 Methods of analysing cluster randomised trials 83 4.1.2 Choosing the statistical method 84 4.2 Aggregate cluster-level analysis – carried out at the cluster level, using aggregate summary data 84 4.3 Statistical methods for continuous outcomes 86 4.3.1 Two independent-samples t-test 86 4.3.2 Example 88 4.4 Mann–Whitney U test 91 4.5 Statistical methods for binary outcomes 94 4.6 Analysis of a matched design 95 4.7 Discussion 98 4.8 Commonly asked question 98 Exercise 99 Appendix 4.A 99 5 Regression methods of analysis for continuous outcomes using individual person-level data 102 5.1 Introduction 102 5.2 Incorrect models 104 5.2.1 The simple (independence) model 104 5.2.2 Fixed effects 104 5.3 Linear regression with robust standard errors 105 5.3.1 Robust standard errors 105 5.3.2 Example of use of robust standard errors 107 5.3.3 Cluster-specific versus population-averaged models 107 5.4 Random-effects general linear models in a cohort study 108 5.4.1 General models 108 5.4.2 Fitting a random-effects model 109 5.4.3 Example of a random-effects model from the PoNDER study 110 5.4.4 Checking the assumptions 110 5.5 Marginal general linear model with coefficients estimated by generalised estimating equations (GEE) 112 5.5.1 Generalised estimating equations 112 5.5.2 Example of a marginal model from the PoNDER study 113 5.6 Summary of methods 114 5.7 Adjusting for individual-level covariates in cohort studies 115 5.8 Adjusting for cluster-level covariates in cohort studies 118 5.9 Models for cross-sectional designs 119 5.10 Discussion of model fitting 120 Exercise 122 Appendix 5.A 123 6 Regression methods of analysis for binary, count and time-to-event outcomes for a cluster randomised controlled trial 126 6.1 Introduction 126 6.2 Difference between a cluster-specific model and a population-averaged or marginal model for binary data 127 6.3 Analysis of binary data using logistic regression 129 6.4 Review of past simulations to determine efficiency of different methods for binary data 130 6.5 Analysis using summary measures 131 6.6 Analysis using logistic regression (ignoring clustering) 132 6.7 Random-effects logistic regression 134 6.8 Marginal models using generalised estimating equations 135 6.9 Analysis of count data 135 6.10 Survival analysis with cluster trials 137 6.11 Missing data 139 6.12 Discussion 139 Exercise 139 Appendix 6.A 140 7 The protocol 143 7.1 Introduction 143 7.2 Abstract 144 7.3 Protocol background 147 7.4 Research objectives 147 7.5 Outcome measures 147 7.6 Design 147 7.7 Intervention details 148 7.8 Eligibility 148 7.9 Randomisation 149 7.10 Assessment and data collection 149 7.11 Statistical considerations 150 7.11.1 Sample size 150 7.11.2 Statistical analysis 151 7.11.3 Interim analyses 152 7.12 Ethics 153 7.12.1 Declaration of Helsinki 153 7.12.2 Informed consent 154 7.13 Organisation 155 7.13.1 The team 155 7.13.2 Trial forms 155 7.13.3 Data management 155 7.13.4 Protocol amendments 156 7.14 Further reading 156 Exercise 156 8 Reporting of cRCTs 159 8.1 Introduction: Extended CONSORT guidelines for reporting and presenting the results from cRCTs 159 8.2 Patient flow diagram 160 8.3 Comparison of entry characteristics 160 8.4 Incomplete data 167 8.5 Reporting the main outcome 171 8.6 Subgroup analysis and analysis of secondary outcomes/endpoints 174 8.7 Estimates of between-cluster variability 175 8.7.1 Example of reporting the ICC: The PoNDER cRCT 175 8.8 Further reading 175 Exercise 176 9 Practical issues 178 9.1 Preventing bias in cluster randomised controlled trials 178 9.1.1 Problems with identifying and recruiting patients to cluster trials 178 9.1.2 Preventing biased recruitment 179 9.2 Developing complex interventions 181 9.3 Choice of method of analysis 182 9.4 Missing data 185 9.5 Example sensitivity analysis: Imputation of missing 6-month EPDS data for at-risk women from the PoNDER cRCT 188 9.6 Multiplicity of outcomes 192 9.6.1 Limiting the number of confirmatory tests 192 9.6.2 Summary measures and statistics 193 9.6.3 Global tests and multiple comparison procedures 193 9.6.4 Which multiple comparison procedure to use? 194 10 Computing software 195 10.1 R 195 10.1.1 History 195 10.1.2 Installing R 196 10.1.3 Simple use of R 197 10.1.4 An example of an R program 198 10.2 Stata (version 12) 199 10.2.1 Introduction to Stata 199 10.2.2 Aggregate cluster-level analysis – carried out at the cluster level, using aggregate summary data 201 10.2.3 Random-effects models – continuous outcomes 202 10.2.4 Random-effects models – binary outcomes 205 10.2.5 Random-effects models – count outcomes 206 10.2.6 Marginal models – continuous outcomes 208 10.2.7 Marginal models – binary outcomes 209 10.2.8 Marginal models – count outcomes 210 10.3 SPSS (version 19) 212 10.3.1 Introduction to SPSS 212 10.3.2 Comparing cluster means using aggregate cluster-level analysis – carried out at the cluster level, using aggregate summary data 213 10.3.3 Marginal models 215 10.3.4 Random-effects models 227 10.4 Conclusion and further reading 232 References 234 Index 243

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Book SynopsisEnsure your clinical trial supply chain is running smoothly with this practical guide Clinical trials are a critical part of the pharmaceutical development process. These trials cannot proceed without timely and regular receipt of the drugs being tested, which can prove a challenge for drug manufacturers who have not yet established the structures required to produce quality-controlled specimens of the drug at scale. Managing supply chains of pre-production drugs for clinical trials is therefore an essential component of drug development. Supply Chain Planning for Clinical Trials offers a practical introduction to this process for researchers and industry professionals. Beginning with the basics of clinical trial supply chain management, it proceeds step by step through all aspects of demand and supply planning for clinical trials. The result is a thorough overview that also offers practical examples of how to plan supply for clinical trials. Supply Chain P

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Book SynopsisA new addition to the popular 'How To' series Shows how to present data in journal articles, grant applications or research presentations correctly and comprehensively Contains numerous examples of good and bad data display Contains examples from many areas of research - including those outside of medicine.Trade Review"This book offers most excitement and is abound with promise." (Urology News, May/June 2009) "The book casts a fresh light on many issues related to effective data presentation. The questions raised and ideas offered are thought-provoking, innovative and easily implemental ... .It is a small but powerful book which I firmly believe everyone would enjoy while reading in addition to learning." (Academici, April 2009) "This book not only provides an enjoyable read, but also it reminds readers how and how not to display data. I strongly recommend this book for both medical researchers and inter-disciplinary readers, including empirical musicology." (Academici, February 2009) “This text would be an excellent primer for those who have the computer background for producing graphics but who lack training in the presentation of material.” (The American Statistician, February 2009) “Effective data presentation is an essential skill … .This should be very helpful to the target audience. Good data presentation should contribute to publication and presentation.” (Doody's Book Reviews) Table of ContentsPreface. 1 Introduction to data display. 2 How to display data badly. 3 Displaying univariate categorical data. 4 Displaying quantitative data. 5 Displaying the relationship between two continuous variables. 6 Data in tables. 7 Reporting study results. 8 Time series plots and survival curves. 9 Displaying results in presentations. Index.

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Book SynopsisNumbers and statistical claims dominate today''s news. Politics, budgets, crime analysis, medical issues, and sports reporting all demand numbers. Now in its third edition, News & Numbers focuses on how to evaluate statistical claims in science, health, medicine, and politics. It does so by helping readers answer three key questions about all scientific studies, polls, and other statistical claims: What can I believe? What does it mean? and How can I explain it to others? Updated throughout, this long overdue third edition brings this classic text up-to-date with the 21st century with a complete updating of examples, case studies, and stories. The text emphasises clear thinking and common sense approaches for understanding, analyzing and explaining statistics, and terms throughout the book are explained in easy-to-understand, nontechnical language. Much new material has been added to ensure the text maintains its pertinent approach to the subject, including: A sTable of ContentsA Note to Our Readers vii A Tribute to Victor Cohn, 1919–2000 ix Foreword xi Acknowledgments xiii Notes on Sources xv Part I Learning the Basics 1 A Guide to Part I of News & Numbers 2 1 Where We Can Do Better 3 2 The Certainty of Uncertainty 8 3 Testing the Evidence 15 4 What Makes a Good Study? 37 5 Your Questions and Peer Review 54 Part II Now Down to Specifics 69 A Guide to Part II of News & Numbers 70 6 Tests and Drug Trials 71 7 Vital Statistics 83 8 Health Costs, Quality, and Insurance 97 9 Our Environment 108 10 Writing About Risks 124 11 Polls 133 12 Statistical Savvy for Many Types of News 145 Epilogue 161 Glossary 163 Bibliography 170 Index 173

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Book SynopsisThis riveting story of the typhus and cholera epidemics that swept through New York City in 1892 has been updated with a new preface that tackles the COVID-19 pandemic. Winner, 2003 Arthur J. Viseltear Prize for Outstanding Book in the History of Public Health, American Public Health AssociationIn Quarantine! Howard Markel traces the course of the typhus and cholera epidemics that swept through New York City in 1892. The story is told from the point of view of those involvedthe public health doctors who diagnosed and treated the victims, the newspaper reporters who covered the stories, the government officials who established and enforced policy, and, most importantly, the immigrants themselves. Drawing on rarely cited stories from the Yiddish American press, immigrant diaries and letters, and official accounts, Markel follows the immigrants on their journey from a squalid and precarious existence in Russia's Pale of Settlement, to their passage in steerage, to New York's Lower East Table of ContentsFigures and Tables Preface to the First EditionPreface to the Updated Edition: Revisiting Quarantine!Introduction: The Concept of QuarantinePart I. Averting a PestilenceThe Typhus Fever Epidemic on New York's Lower East SideChapter 1. The Russian Jews of the SS MassiliaChapter 2. The City Responds to the Threat of TyphusChapter 3. The Results of the Quarantine Part II. "Cholera May Knock, but It Won't Get In!"Cholera, Class, and Quarantine in New York HarborChapter 4. Awaiting the Cholera: "Choleria!"Chapter 5. "Knocking Out the Cholera!"Part III. Legislating QuarantineAttempting to Restrict Immigration as a Cholera PreventiveChapter 6. Maintaining the QuarantineChapter 7. The Doctors' Prescription for QuarantineChapter 8. The Congress RespondsEpilogue: "The Microbe as Social Leveller"NotesIndex

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Book SynopsisThe pandemic presented religion as a paradox: faith is often crucial for helping people weather life’s troubles and make difficult decisions, but how can religion continue to deliver these benefits and provide societal structure without social contact? The topical volume, An Epidemic among My People explains how the COVID-19 pandemic stress tested American religious communities and created a new politics of religion centered on public health.The editors and contributorsconsider how the virus and government policy affected religion in America. Chapters examine the link between the prosperity gospel and conspiracy theories, the increased purchase of firearms by evangelicals, the politics of challenging public health orders as religious freedom claims, and the reactions of Christian nationalists, racial groups, and female clergy to the pandemic (and pandemic politics). As sharp lines were drawn between people and their governments during this uncertain time, Trade Review“What power does religion hold in times of crisis? Drawing on a wealth of research conducted over the first two years of the coronavirus pandemic, the thoughtful editors and contributors to An Epidemic among My People uncover how faith communities and religious identities shaped responses to the pandemic. At the same time, the broad range of research contained here underscores how a crisis impacts religious beliefs and practices. This unique collection of essays from across the social sciences showcases findings and insights essential to understanding how religious forces matter in our collective experience of major national and worldwide events.”—Janelle Wong, Professor of American Studies and Government and Politics at the University of Maryland, and author of Immigrants, Evangelicals, and Politics in an Era of Demographic Change“COVID-19 had colossal impacts on public health and mortality, with immense social, political, economic, and psychological consequences. Our understanding of its more precise consequences in particular spheres of society remains thin. An Epidemic among My People offers a reliable, in-depth account of the impacts of COVID-19 on one major area of social life—religion. An impressive array of scholars use very strong empirical data to insightfully sort out the many ways religion and the pandemic interacted, and with what consequences. It is essential reading not only on religion, but for anyone wishing to understand the impact of COVID-19 in society generally.”—Christian Smith, Wm. R. Kenan Jr. Professor of Sociology at the University of Notre Dame "The book covers a lot of territory, with unexpected findings throughout....Through its remarkable collection of new research and data analysis, this book provides a broad foundation for scholars to build on to better understand the complex, multifaceted relationship between the Covid-19 pandemic and American religion and politics.”— Politics and Religion

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Book SynopsisThis very popular textbook, now in its third edition, offers an accessible description of fundamental and more advanced concepts and methods of logistic regression. This edition includes three new chapters and an expanded section about modeling guidelines.Trade ReviewFrom the book reviews:“The authors present fundamental and basic ideas and methods of analysis of survival/event-history data from both applications and methodological points of view. … This book is clearly written and well structured for a graduate course as well as for practitioners and consulting statisticians. … There are many good examples in this edition, and more importantly, this new edition offers additional exercises, making it a good candidate for adoption as a textbook.” (Technometrics, August, 2012)"This text is … an elementary introduction to survival analysis. It is primarily intended for self-study, but it has also proven useful as a basic text in a standard classroom course … . Each chapter starts with an Introduction, an Abbreviated outline, and Objectives, and ends with self tests, exercises and a detailed outline. Solutions to tests and exercises are also provided." (Göran Broström, Zentralblatt MATH, Vol. 1093 (19), 2006)"The most meaningful accolade that I can give to this text is that it admirably lives up to its title." Journal of the American Statistical Association, September 2006"Imagine---a statistics textbook that actually explains things in English instead of explaining a topic by bombarding the reader with page-width equations requiring an advanced degree in Math just to read the book. If it weren't for this book, I would be really stuck." (David Britz)Table of ContentsIntroduction to Survival Analysis.- Kaplan-Meier Survival Curves and the Log-Rank Test.- The Cox Proportional Hazards Model and Its Characteristics.- Evaluating the Proportional Hazards Assumption.- The Stratified Cox Procedure.- Extension of the Cox Proportional Hazards Model for Time-Dependent Variables.- Parametric Survival Models.- Recurrent Events Survival Analysis.- Competing Risks Survival Analysis.

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Book SynopsisEpidemiology Kept Simple introduces the epidemiological principles and methods that are increasingly important in the practice of medicine and public health. With minimum use of technical language it fully explains terminology, concepts, and techniques associated with traditional and modern epidemiology. Topics include disease causality, epidemiologic measures, descriptive epidemiology, study design, clinical and primary prevention trials, observational cohort studies, case-control studies, and the consideration of random and systematic error in studies of causal factors. Chapters on the infectious disease process, outbreak investigation, and screening for disease are also included. The latter chapters introduce more advanced biostatistical and epidemiologic techniques, such as survival analysis, Mantel-Haenszel techniques, and tests for interaction. This third edition addresses all the requirements of the American Schools of Public Health (ASPH) Epidemiological CompetTrade Review"This edition does a good job of updating the previous editions, which have not covered the ASPH epidemiology competencies." (Doody’s, 21 February 2014)Table of ContentsPreface to the Third Edition xi Preface to the First Edition xiii Acknowledgments xv 1 Epidemiology Past and Present 1 1.1 Epidemiology and its uses 2 1.2 Evolving patterns of morbidity and mortality 5 1.3 Selected historical figures and events 8 1.4 Chapter summary 30 Review questions 31 References 32 2 Causal Concepts 36 2.1 Natural history of disease 36 2.2 Variability in the expression of disease 40 2.3 Causal models 41 2.4 Causal inference 48 Exercises 58 Review questions 61 References 63 3 Epidemiologic Measures 66 3.1 Measures of disease frequency 67 3.2 Measures of association 74 3.3 Measures of potential impact 79 3.4 Rate adjustment 82 Exercises 90 Review questions 98 References 99 Addendum: additional mathematical details 101 4 Descriptive Epidemiology 104 4.1 Introduction 104 4.2 Epidemiologic variables 108 4.3 Ecological correlations 116 Exercises 121 Review questions 123 References 124 5 Introduction to Epidemiologic Study Design 126 5.1 Etiologic research 126 5.2 Ethical conduct of studies involving human subjects 129 5.3 Selected study design elements 130 5.4 Common types of epidemiologic studies 137 Exercises 138 Review questions 140 References 141 6 Experimental Studies 142 6.1 Introduction 142 6.2 Historical perspective 144 6.3 General concepts 146 6.4 Data analysis 152 Exercises 156 Review questions 157 References 157 7 Observational Cohort Studies 159 7.1 Introduction 159 7.2 Historical perspective 161 7.3 Assembling and following a cohort 163 7.4 Prospective, retrospective, and ambidirectional cohorts 164 7.5 Addressing the potential for confounding 165 7.6 Data analysis 166 7.7 Historically important study: Wade Hampton Frost’s birth cohorts 170 Exercises 174 Review questions 177 References 177 8 Case–Control Studies 180 8.1 Introduction 180 8.2 Identifying cases and controls 182 8.3 Obtaining information on exposure 185 8.4 Data analysis 186 8.5 Statistical justifications of case–control odds ratio as relative risks 193 Exercises 194 Review questions 198 References 199 9 Error in Epidemiologic Research 201 9.1 Introduction 201 9.2 Random error (imprecision) 203 9.3 Systematic error (bias) 209 Exercises 217 Review questions 219 References 220 10 Screening for Disease 222 10.1 Introduction 223 10.2 Reliability (agreement) 224 10.3 Validity 228 Summary 238 Exercises 239 Review questions 243 References 243 10.4 Chapter addendum (case study) 244 Further reading—screening for HIV 248 Further reading—general concepts of screening 248 Answers to case study: screening for antibodies to the human immunodeficiency virus 249 11 The Infectious Disease Process 255 11.1 The infectious disease process 255 11.2 Herd immunity 265 Exercises 267 Review questions 268 References 270 12 Outbreak Investigation 271 12.1 Background 272 12.2 CDC prescribed investigatory steps 273 Review questions 282 References 283 References—a drug–disease outbreak 286 13 Confidence Intervals and p-Values 302 13.1 Introduction 303 13.2 Confidence intervals 304 13.3 p-Values 312 13.4 Minimum Bayes factors 319 References 322 14 Mantel–Haenszel Methods 323 14.1 Ways to prevent confounding 323 14.2 Simpson’s paradox 325 14.3 Mantel–Haenszel methods for risk ratios 325 14.4 Mantel–Haenszel methods for other measures of association 329 Exercise 335 References 335 15 Statistical Interaction: Effect Measure Modification 337 15.1 Two types of interaction 337 15.2 Chi-square test for statistical 340 15.3 Strategy for stratified analysis 342 Exercises 344 References 345 16 Case Definitions and Disease Classification 347 16.1 Case definitions 347 16.2 International classification of disease 351 16.3 Artifactual fluctuations in reported rates 353 16.4 Summary 354 References 355 17 Survival Analysis 356 17.1 Introduction 356 17.2 Stratifying rates by follow-up time 359 17.3 Actuarial method of survival analysis 360 17.4 Kaplan–Meier method of survival analysis 362 17.5 Comparing the survival experience of two groups 364 Exercises 369 References 371 18 Current Life Tables 373 18.1 Introduction 373 18.2 Complete life table 374 18.3 Abridged life table 380 Exercises 383 References 384 19 Random Distribution of Cases in Time and Space 385 19.1 Introduction 385 19.2 The Poisson distribution 386 19.3 Goodness of fit of the Poisson distribution 390 19.4 Summary 394 Exercises 395 References 396 Answers to Exercises and Review Questions 398 Appendix 1: 95% Confidence Limits for Poisson Counts 434 Appendix 2: Tail Areas in the Standard Normal (Z) Distribution: Double These Areas for Two-Sided p-Values 436 Appendix 3: Right-Tail Areas in Chi-Square Distributions 439 Appendix 4: Case Study—Cigarette Smoking and Lung Cancer 441 Appendix 5: Case Study—Tampons and Toxic Shock Syndrome 448 Index 455

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Book SynopsisA lively and comprehensive review of policy change, Multidisciplinary public health: Understanding the development of the modern workforce concludes with a reflection on the new public health system under way in England, making useful comparisons with the rest of the UK.Trade Review"Recent developments in public health are poorly understood by the public. Multidisciplinary public health provides a readable history, based on the authors' own involvement, of one key change in modern public health - the incorporation of non-medical people into the mainstream public health workforce." Virginia Berridge, London School of Hygiene and Tropical Medicine "Public health in England has moved into uncharted territory. This timely and important history of the changing workforce is an indispensable guide to the challenges and unfinished business ahead" David J Hunter, Durham University "A key text, entertainingly and expertly written, for anyone who wants to be better informed about public health and the contemporary development of the workforce that delivers it. This book provides a welcome addition to the literature on public health in England" Lord Hunt of King's HeathTable of ContentsIntroduction and methods; Developing the specialty of public health; The multidisciplinary public health movement of the 1990s; Changes for specialists I: Setting up a multidisciplinary public health senior appointments process; Changes for specialists II: The new regulatory system for specialists; Changes for specialists III: The establishment of multidisciplinary higher specialist training in public health; The focus on practitioners and the wider workforce; Where we are now? The new public health system in England from April 2013; Experience across the other UK countries; Conclusion.

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Book SynopsisThrough the framework of understanding health inequalities as a 'wicked problem' the book develops an applied approach to researching, understanding and addressing these by drawing on complexity theory.Trade Review"An absolute 'must read' for health-care practitioners and social scientists, this book makes a compelling case for 'the way forward' for policy makers." Brian Castellani, Kent State University"An excellent overview of research on health inequality and measures to reduce them. The examples, while drawn mainly from the UK, have international relevance for the debate about 'what works' in tackling these inequalities." Sarah Curtis, FBA, Professor of Health and Risk, Durham University“This is a very important book and a must-read for anyone interested in doing applied social science in today’s political climate where evidence and complexity matter.” Emma Uprichard, Warwick UniversityTable of ContentsIntroduction: Part one: Context and theory: developing an applied approach to studying health inequalities; Health inequalities, wicked problems and complexity; Health inequalities: adopting a whole systems approach; Measuring health inequalities; Part two: health inequalities in England; A history of health inequalities in England; Health inequalities post 2010; Part Three: Case studies; Evidence for public health practice: Health Inequalities National Support Team (Professor Chris Bentley and Peter Counsell); Qualitative Comparative Analysis case study; Part 4: Conclusion; Conclusion.

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