Clinical trials Books

Book SynopsisPacy, dark and highly entertaining, with a real sense of menace' Sunday Times bestseller, T.M. LoganThe very definition of a white-knuckle ride' Sunday Times bestseller, B P WalterBrilliant . . . a proper page-turner' Sunday Times bestseller, Laura MarshallWould you sign up to a medical trial if you didn't know the possible side effects?18-40? PAID CLINICAL TRIAL IN THE CANARY ISLANDS UP TO 20,000 TAX FREE It seems like the opportunity of a lifetime. An all-inclusive luxury trip abroad, all you need to do is take a pill every day and keep a diary.Except you don't know anything about the drug or what its side effects might be.The headaches start, a dull ache at first. Every day worse than the last.Then a body is found.Everyone is a suspect. Anyone could be a killer. Even you . . .The up-all-night thriller perfect for fans of Alex Michaelides, T. M. Logan and JP DelaneyEarly readers have signed up for The Trial . . . will you?Such a cool premise . . . a page-turner that kept me gripped'Trade Review‘Intelligent and thought-provoking’ Victoria Selman ‘Smart, fast and scary. For any reader, it’s an unforgettable trip’ Rachel Edwards ‘An intense mystery which moves and twists and left me always off-balance. Full of foreboding and vivid characters’ James Delargy

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Book SynopsisHelps you explore how practitioners established a new style of practice, at the center of which lies the cancer clinical trial. Far from mere testing devices, this book features trials that have become full-fledged experiments that have redefined the practices of clinicians, statisticians, and biologists.

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Book SynopsisPraise for the Second Edition:â this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study. -BiometricsThis impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered â â Journal of the Royal Statistical SocietySample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, thisTrade ReviewPraise for the Second Edition:"… this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics"This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered …" – Journal of the Royal Statistical Society"The book is nicely set out with an introduction to the basic idea of each topic, followed by various formulae that lead to power calculations . . . In all, I consider this book to be well written, and it touches on quite a number of more recent topics in sample size determination. Consequently, it will be a useful addition to clinical statisticians as a point of reference to solve more complex issues in power calculations during the design of a clinical trial." – Steve Su, International Society for Clinical BiostatisticsSample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation.Features: Compares the relative merits and disadvantages of statistical methods for sample size calculations Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and women’s health Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.Praise for the Second Edition:"… this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics"This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered …" – Journal of the Royal Statistical Society"The book is nicely set out with an introduction to the basic idea of each topic, followed by various formulae that lead to power calculations . . . In all, I consider this book to be well written, and it touches on quite a number of more recent topics in sample size determination. Consequently, it will be a useful addition to clinical statisticians as a point of reference to solve more complex issues in power calculations during the design of a clinical trial." – Steve Su, International Society for Clinical BiostatisticsSample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation.Features: Compares the relative merits and disadvantages of statistical methods for sample size calculations Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and women’s health Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.Table of ContentsIntroduction. Considerations Prior to Sample Size Calculation. Comparing Means. Large Sample Tests for Proportions. Exact Tests for Proportions. Tests for Goodness-of-Fit and Contingency Tables. Comparing Time-to-Event Data. Group Sequential Methods. Comparing Variabilities. Bioequivalence Testing. Dose Resonse Studies. Microarray Studies. Bayesian Sample Size Calculation. Nonparametrics. Cluster Randomized Design. Sample Size Calculation for Two-Stage Adaptive Design. Sample Size for Clinical Trials with Extremely Low Incidence Rates. Clinical Trial Simulation for Sample Size Estimation. Sample Size Calculation in Other Areas.

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Book SynopsisReal-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance, real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives, examples, and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare, with a focus on the pharmaceutical industry.This book presents an overview of key analytical issues and best practices. Special attention is paid to the development, methodologies, and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation.Features: Provides an overview Table of ContentsPreface: Real World Evidence and Digital Innovation to Combat Noncommunicable Diseases. 1. Real World Evidence Generation. 2. Applications of RWE for Regulatory Uses. 3. Ethics & Bioethics. 4. Real- World Data, Big Data and Artificial Intelligence: Recent Development and Emerging Trends in the European Union. 5. Patient centricity and Precision Medicine. 6. Health Information Technology. 7. Digital Health Technologies and Innovations. 8. Economic Analysis and Outcome Assessment. 9. Partnerships and Collaborations. 10. Global Perspective: China Big Data Collaboration to Improve Patient Care. 11. The Future of Patient-Centric Data-Driven Healthcare

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Book SynopsisThis book aims to set out the political, social, legal and scientific underpinning of risk assessment and risk management for toxic substances. It describes the principles and processes the practitioners undertake when looking at the regulatory risk implications of their work.Table of ContentsPreface -- Acknowledgements -- 1 Introduction -- PART I -- The context in which toxic risk analysis takes place -- 2 What risk management covers -- 3 Legal and organisational frameworks -- 4 Philosophical frameworks for handling risk -- 5 The importance of risk perception and risk -- communication for toxicological risk assessment -- PART II -- The principles and practice of toxic risk analysis -- 6 Introduction: Royal Society and National Academy of Sciences -- 7 Toxicological assessment -- 8 Evaluation of human health effects: toxicity -- 9 Evaluation of human health effects: exposure -- 10 The special case of major accident hazards -- 11 Evaluation of effects on the environment -- 12 Effects on the atmosphere -- References -- Appendix -- Index.

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Book SynopsisClinical Trials in Neurology provides the tools to enhance the development of new treatments for neurologic diseases through introducing the reader to key concepts underpinning trials in the neurosciences. This comprehensive guide is essential reading for neurologists, psychiatrists, neurosurgeons, neuroscientists, statisticians and clinical researchers in the pharmaceutical industry.Table of ContentsPreface; Part I. The Role of Clinical Trials in Therapy Development: 1. The impact of clinical trials in neurology E. Ray Dorsey and S. Claiborne Johnston; 2. The sequence of clinical development Michael Poole; 3. Unique challenges in the development of therapies for neurological disorders Gilmore N. O'Neill; Part II. Concepts in Biostatistics and Clinical Measurement: 4. Fundamentals of biostatistics Judith Bebchuk and Janet Wittes; 5. Bias and random error Susan S. Ellenberg and Jacqueline A. French; 6. Approaches to data analysis William R. Clarke; 7. Selecting outcome measures Robert G. Holloway and Andrew D. Siderowf; Part III. Special Study Designs and Methods for Data Monitoring: 8. Selection and futility designs Bruce Levin; 9. Adaptive designs across stages of therapeutic development Christopher S. Coffey; 10. Crossover designs Mary E. Putt; 11. Two period designs for evaluation of disease-modifying treatments Michael P. McDermott; 12. Enrichment designs Kathryn M. Kellogg and John Markman; 13. Non-inferiority trials Rick Chappell; 14. Monitoring of clinical trials: interim monitoring, data monitoring committees, and group sequential methods applied to neurology Rickey E. Carter and Robert F. Woolson; 15. Clinical approaches to post-marketing drug safety assessment Gerald J. Dal Pan; Part IV. Ethical Issues: 16. Ethics in clinical trials involving the central nervous system: risk, benefit, justice, and integrity Jonathan Kimmelman; 17. The informed consent process: compliance and beyond Scott Y. H. Kim; Part V. Regulatory Perspectives: 18. Evidentiary standards for neurologic drugs and biologics approval Russell Katz; 19. Premarket reviews of neurological devices Eric A. Mann and Peter G. Como; Part VI. Clinical Trials in Common Neurological Disorders: 20. Parkinson's disease Karl D. Kieburtz and Jordan Elm; 21. Alzheimer's disease Joshua Grill and Jeffrey Cummings; 22. Acute ischemic stroke Karen C. Johnston, Devin L. Brown and Yuko Y. Palesch; 23. Multiple sclerosis Richard A. Rudick, Elizabeth Fisher and Gary R. Cutter; 24. Amytrophic lateral sclerosis (ALS) Nazem Atassi, David Schoenfeld and Merit Cudkowicz; 25. Epilepsy John R. Pollard, Susan S. Ellenberg and Jacqueline A. French; 26. Insomnia Michael E. Yurcheshen, Changyong Feng and J. Todd Arnedt; Part VII. Clinical Trial Planning and Implementation: 27. Clinical trial planning: an academic and industry perspective Cornelia L. Kamp and Jean-Michel Germain; 28. Clinical trial implementation, analysis and reporting: an academic and industry perspective Cornelia L. Kamp and Jean-Michel Germain; 29. Academic-industry collaborations and compliance issues Troy Morgan; Index.

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Book SynopsisTrade Review"Winner of the 2018 Don K. Price Award, Science, Technology & Environmental Politics Section of the American Political Science Association""Winner of the 2018 Louis Brownlow Book Award, National Academy of Public Administration"

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Book SynopsisCovering areas of drug toxicity which address the major issues including registration requirements of new drugs and pharmacovigilance, this book provides an overview of the methodology and requirements of pre-clinical safety assessment of new medicines. It discusses mechanisms by which drugs cause toxic effects in living organisms.Trade Review'The text is well written, concise and easy to understand with informative and helpful illustrations, figures and tables...it serves as an excellent companion to pharmacology textbooks, dealing with the issues relevant for assessing the safety of new drugs...a good starting point for the novice toxicologist, ambitious undergraduate student or graduate students working in the pharmaceutical sciences.' Irish Pharmacy Journal, November 2006, p418 * Irish Pharmacy Journal *'...this is a great little book...the text is aimed primarily at new PHD students in the pharmaceutical and related sciences but could equally well serve taught postgraduate students on toxicology, clinical pharmacology, drug development and safety or similar courses. The book 'does exactly what it says on the tin' and will definitely whet the appetite of enthusiastic young researchers for toxicology.' Heather M Wallace, Summer 2007 issue of the British Toxicology Society Newsletter -- Heather M Wallace * British Toxicology Society Newsletter *Table of Contents1. General toxicology; 2. Drug metabolism: inactivation and bioactivation of xenobioptics; 3. Molecular and cellular mechanisms of toxicity; 4. Teratology; 5. Genotoxicity; 6. Carcinogenicity of drugs; 7. Liver toxicity; 8. Kidney toxicity; 9. Toxicology in the respiratory system; 10. Immunotoxicity; 11. Clinical toxicology; 12. Safety assessment of pharmaceuticals: regulatory aspects; 13. Pharmacovigilance.

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Book SynopsisThis revised and updated new edition of a best-selling text remains a fast and convenient overview of the clinical trials in gynecologic cancer treatment, outlining the evidence base of treatment decisions in uterine, ovarian, cervical, and vulvar cancers, and gestational trophoblastic neoplasia. Residents and fellows will find this book an indispensable reference, while practitioners will welcome it as a clarification of the evidence base for treatment options. *Gives a convenient summary of trials in gynecologic oncology *Supplies an invaluable revision primer for those undertaking certification *Provides a uniquely up-to-date resourceTable of ContentsPreface to the second edition1. Uterine Malignancies. 1.1. Endometrial Carcinoma. 1.1.1. Studies addressing surgical treatment. 1.1.1.1. Lymph node assessment 1.1.2. Studies addressing adjuvant therapy. 1.1.2.1. Radiation. 1.1.2.2. Chemoradiation. 1.1.2.3. Chemotherapy. 1.1.3. Studies addressing treatment of recurrent endometrial carcinoma. 1.1.3.1. Hormonal therapy. 1.1.3.2. Chemotherapy. 1.1.3.3. Targeted therapy. 1.1.3.4. Immunotherapy. 1.2. Uterine Carcinosarcoma. 1.2.1. Studies addressing prognosticators. 1.2.2. Studies addressing adjuvant treatment. 1.2.3. Studies addressing treatment of advanced/ recurrent uterine carcinosarcoma. 1.3. Uterine Leiomyosarcoma. 1.3.1. Studies addressing adjuvant treatment of early stage uterine leiomyosarcoma. 1.3.2. Studies addressing treatment of advanced uterine leiomyosarcoma. 1.4. References.2. Ovarian Malignancies. 2.1. Epithelial ovarian cancer (EOC). 2.1.1. Studies addressing upfront surgery. 2.1.2. Studies addressing adjuvant therapy. 2.1.3. Studies addressing intraperitoneal chemotherapy. 2.1.4. Studies addressing first line maintenance treatment. 2.1.5. Studies addressing neoadjuvant therapy. 2.1.6. Studies addressing prediction of debulking. 2.1.7. Studies addressing time point of treatment of recurrence. 2.1.8. Studies addressing treatment of recurrent ovarian cancer, platinum-sensitive. 2.1.8.1. Chemotherapy and targeted therapy. 2.1.8.2. Secondary debulking surgery. 2.1.9. Studies addressing treatment of recurrent ovarian cancer, platinum-resistant. 2.1.9.1. Chemotherapy and targeted therapy. 2.1.9.2. Immunotherapy. 2.2. Low grade serous ovarian cancer. 2.3. Mucinous epithelial ovarian cancer (mEOC). 2.4. Ovarian Germ Cell Tumors (GCT). 2.5. Ovarian Sex Cord-Stromal Tumors (SCT). 2.6. References.3. Cervical Cancer. 3.1. Studies addressing surgical treatment. 3.2. Studies addressing adjuvant therapy. 3.3. Studies addressing primary chemoradiation therapy. 3.4. Studies addressing treatment of recurrent or metastatic cervical cancer. 3.4.1. Chemotherapy and targeted therapy. 3.4.2. Immunotherapy. 3.5. References.4. Vulvar Cancer. 4.1. Studies addressing surgical treatment. 4.1.1. Groin lymph nodes. 4.2. Studies addressing advanced vulvar cancer. 4.2.1. Chemoradiation. 4.2.2. Immunotherapy. 4.3. References.5. Gestational Trophoblastic Neoplasia (GTN). 5.1. Studies addressing surgical treatment. 5.2. Studies addressing chemotherapy in low-risk GTN. 5.3. Studies addressing (chemo)therapy in high-risk GTN. 5.4. References.6. Glossary. Index.

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Book SynopsisReal-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance, real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives, examples, and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare, with a focus on the pharmaceutical industry.This book presents an overview of key analytical issues and best practices. Special attention is paid to the development, methodologies, and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation.Features: Provides an overview

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Book SynopsisCausal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. This book covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, longitudinal studies, singlearm clinical trials with external controls, and real-world evidence studies. The book starts with the central questions in drug development and licensing, takes the reader through the basic concepts and methods via different study types and through different stages, and concludes with a roadmap to conduct causal inference in clinical studies. The book is intended for clinical statisticians and epidemiologists working in the pharmaceutical industry. It will also be useful to graduate students in statistics, biostatistics, and data science looking to pursue a career in the pharmaceutical industry.Key Featur

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Book SynopsisThis book serves as a comprehensive guide on quality control and regulatory aspects for biological products. It covers a wide range of topics, including regulatory requirements, quality control strategies, analytical methods, and risk management. It delves into the advantages and limitations of in vivo tests and discusses alternative methods that can be employed. The book explores the use of animal-based testing methods in quality control and examines viable alternatives.Key Features: Reviews various scientific and regulatory aspects involved in the quality control of biologicals Provides an overview of the roles of various national and international regulatory bodies and accreditation agencies Presents advanced analytical methods, innovative technologies, and the integration of molecular diagnostics in quality control processes Explores the use of animal-based testing methods in quality control, as well as their alternative

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Book SynopsisWith a focus on estimating the success probability of an experiment, this book provides an introduction to the various statistical techniques involved in medical research and drug development addressing the theoretical and practical aspects of the topic.Table of ContentsPreface xv Acknowledgments xvii Acronyms xix Introduction xxi I.1 Overview of clinical trials xxii I.2 Success rates of clinical trials xxiv I.3 Success probability xxv I.4 Starting from practice xxvii PART I SUCCESS PROBABILITY ESTIMATION IN PLANNING AND ANALYZING CLINICAL TRIALS 1 Basic statistical tools 3 1.1 Pointwise estimation 4 1.2 Confidence interval estimation, conservative estimation 6 1.3 The statistical hypotheses, the statistical test and the type I error for one-tailed tests 10 1.4 The power function and the type II error 11 1.5 The p-value 14 1.6 The success probability and its estimation 17 1.7 Basic statistical tools for two tailed tests 19 1.8 Other statistical hypotheses and tests 23 2 Reproducibility Probability Estimation 25 2.1 Pointwise RP estimation 26 2.2 RP-testing 29 2.3 The RP estimate and the p-value 32 2.4 Statistical lower bounds for the RP 35 2.5 The stability criterion for statistical significance 37 2.6 Other stability criteria for statistical significance 40 2.7 Comparing stability criteria 43 2.8 Regulatory agencies and the single study 45 2.9 The RP for two-tailed tests 46 2.10 Discussing Situation I in Section I.4.1 49 3 Sample Size Estimation 51 3.1 The classical paradigm of sample size determination 52 3.2 SP estimation for adapting the sample size 55 3.3 Launching the trial in practice 57 3.4 Practical aspects of SSE 60 3.5 Frequentist conservative SSE 67 3.6 Optimal frequentist CSSE 70 3.7 Bayesian CSSE 75 3.8 A comparison of CSSE strategies 80 3.9 Discussing Situations I and II in Section I.4 83 3.10 Sample size estimation for the two-tailed setting 85 4 Robustness and Corrections in Sample Size Estimation 89 4.1 CSSE strategies with different effect sizes in phases II and III 90 4.2 Comparing CSSE strategies in different Scenarios 91 4.3 Corrections for CSSE strategies 94 4.4 A comparison among Corrected CSSE strategies 97 PART II SUCCESS PROBABILITY ESTIMATION FOR SOME WIDELY USED STATISTICAL TESTS 5 General parametric SP estimation 105 5.1 The parametric model 105 5.2 Power, SP and noncentrality parameter estimation 106 5.3 RP estimation and testing 107 5.4 Sample size estimation 108 5.5 Statistical tests included in the model 109 6 SP estimation for Student’s t statistical tests 113 6.1 Test for two means equal variances 114 6.1.1 Power and RP estimation 114 6.2 Test for two means unequal variances 117 6.3 On Student’s t RP estimates 120 7 SP estimation for Gaussian distributed test statistics 123 7.1 Test for two proportions 123 7.2 Test for survival: the log-rank test 127 8 SP estimation for Chi-square statistical tests 133 8.1 Test for two multinomial distributions: 2 x C comparative trial 133 8.2 Test for S couples of binomial distributions: the Mantel-Haenszel test 137 8.3 On χ2 RP estimates 141 9 General nonparametric SP estimation with - applications to the Wilcoxon test 143 9.1 The nonparametric model 144 9.2 General nonparametric SP estimation 145 9.3 The Wilcoxon rank-sum test 146 A Tables of quantiles 161 B Tables of RP estimates for the one-tailed Z test 169 References 179 Topic index 185 Author index 193

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Book SynopsisDesigned as a practical, succinct guide, for quick reference by clinicians with everyday questions, this title guides the reader through the range of approaches available for diagnosis, management, or prevention of hemorrhagic and thrombotic diseases or disorders.Table of ContentsList of Contributors ix 1 Basic Principles Underlying Coagulation 1 Dougald M. Monroe 2 Laboratory Tests of Hemostasis 12 Steve Kitchen and Michael Makris 3 Molecular Diagnostic Approaches to Hemostasis 27 Paula James and David Lillicrap 4 Tests of Platelet Function 42 Marie Lordkipanidzé, Gillian C. Lowe and Paul Harrison 5 Evaluation of the Bleeding Patient 63 Alice Ma and Marshall Mazepa 6 Hemophilia A and B 79 Rhona M. Maclean and Michael Makris 7 Von Willebrand disease 94 Giancarlo Castaman, Alberto Tosetto and Francesco Rodeghiero 8 The Rarer Inherited Coagulation Disorders 113 Paula H.B. Bolton‐Maggs, Jonathan Wilde and Gillian N. Pike 9 Acquired Inhibitors of Coagulation 124 Riitta Lassila and Elina Armstrong 10 Quantitative Platelet Disorders 134 Riten Kumar and Walter H.A. Kahr 11 Qualitative Platelet Disorders 160 Eti A. Femia Gian Marco Podda and Marco Cattaneo 12 Disseminated Intravascular Coagulation 172 Raj S. Kasthuri and Nigel S. Key 13 Thrombotic Microangiopathies 183 Marie Scully and David Kavanagh 14 Venous Thromboembolism 195 Sarah Takach Lapner, Lori-Ann Linkins and Clive Kearon 15 Myeloproliferative Neoplasms: Thrombosis and Hemorrhage 210 Brandi Reeves and Stephan Moll 16 Arterial Thrombosis 229 R. Campbell Tait and Catherine N. Bagot 17 Anticoagulation: Heparins and Vitamin K Antagonists 240 Gualtiero Palareti and Benilde Cosmi 18 The Direct Oral Anticoagulants 253 David A. Garcia, Mark A. Crowther and Walter Ageno 19 Antiphospholipid Syndrome 269 Henry G. Watson and Mark Crowther 20 Cardiovascular Medicine 282 Sreekanth Vemulapalli and Richard C. Becker 21 Cardiothoracic Surgery 299 Denise O’Shaughnessy and Ravi Gill 22 Neurology 314 Michael Wang, Natalie Aucutt-Walter, Valerie L. Jewells and David Y. Huang 23 Hepatology 329 Lara N. Roberts, Raj K. Patel and Roopen Arya 24 Nephrology 340 Vimal K. Derebail and Thomas L. Ortel 25 Oncology 355 Anna Falanga and Marina Marchetti 26 Obstetrics, Contraception, and Estrogen Replacement 379 Amy Webster and Sue Pavord 27 Pediatrics 393 Mary E. Bauman, Aisha Bruce and M. Patricia Massicotte 28 Intensive and Critical Care 414 Beverley J. Hunt 29 Transfusion 433 Adrian Copplestone Appendix 1 Reference Ranges 444 Steve Kitchen and Michael Makris Index 452

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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 SynopsisPraise for the First Edition All medical statisticians involved in clinical trials should read this book - Controlled Clinical Trials Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians. Randomization in Clinical Trials: Theory and Practice, Second Edition features: Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials A new chapter on covariate-adaptive randomization, including minimization techniques and inference New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asyTrade Review"Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, the book is the go-to guide for biostatisticians and pharmaceutical industry statisticians." (Zentralblatt MATH 2016)Table of ContentsPreface xi 1 Randomization and the Clinical Trial 1 1.1 Introduction 1 1.2 Causation and Association 2 1.3 Randomized Clinical Trials 6 1.4 Ethics of Randomization 9 1.5 Problems 12 1.6 References 13 2 Issues in the Design of Clinical Trials 15 2.1 Introduction 15 2.2 Study Outcomes 15 2.3 Sources of Bias 18 2.3.1 Selection and ascertainment bias 19 2.3.2 Statistical analysis philosophy 20 2.3.3 Losses to follow-up and noncompliance 21 2.3.4 Covariates 21 2.4 Experimental Design 23 2.5 Recruitment and Follow-Up 25 2.6 Determining the Number of Randomized Subjects 26 2.6.1 Development of the main formula 27 2.6.2 Example 29 2.6.3 Survival trials 29 2.6.4 Adjustment for noncompliance 32 2.6.5 Additional considerations 32 2.7 Problems 33 2.8 References 34 3 Randomization for Balancing Treatment Assignments 37 3.1 Introduction 37 3.2 Complete Randomization 38 3.3 Forced Balance Procedures 40 3.3.1 Random allocation rule 40 3.3.2 Truncated binomial design 42 3.3.3 Hadamard randomization 44 3.3.4 Maximal procedure 46 3.4 Forced Balance Randomization Within Blocks 46 3.4.1 Permuted block design 46 3.4.2 Random block design 47 3.5 Efron’s Biased Coin Design 48 3.6 Other Biased Coin Designs and Generalizations 51 3.7 Wei’s Urn Design 52 3.8 Other urn Models and Generalizations 54 3.9 Comparison of Balancing Properties 55 3.10 Restricted Randomization for Unbalanced Allocation 58 3.11 K > 2 Treatments 61 3.12 Problems 62 3.13 References 64 3.14 Appendix 66 4 The Effects of Unobserved Covariates 67 4.1 Introduction 67 4.2 A Bound on the Probability of a Covariate Imbalance 68 4.3 Simulation Results 70 4.4 Accidental Bias 71 4.5 Maximum Eigenvalue of 𝚺T 73 4.6 Accidental Bias for Biased Coin Designs 74 4.7 Chronological Bias 75 4.8 Problems 76 4.9 References 76 4.10 Appendix 77 5 Selection Bias 79 5.1 Introduction 79 5.2 The Blackwell–Hodges Model 81 5.3 Predictability of a Randomization Sequence 83 5.4 Selection Bias for the Random Allocation Rule and Truncated Binomial Design 84 5.5 Selection Bias in a Permuted Block Design 87 5.5.1 Permuted blocks using the random allocation rule 87 5.5.2 Permuted blocks with truncated binomial randomization 87 5.5.3 Random block design 87 5.5.4 Conclusions 89 5.6 Selection Bias for Other Restricted Randomization Procedures 90 5.6.1 Efron’s biased coin design 90 5.6.2 Wei’s urn design 90 5.6.3 Smith’s design 91 5.7 Simulation Results 91 5.8 Controlling and Testing for Selection Bias in Practice 93 5.9 Problems 94 5.10 References 94 5.11 Appendix 95 6 Randomization as a Basis for Inference 97 6.1 Introduction 97 6.2 The Population Model 97 6.3 The Randomization Model 100 6.4 Randomization Tests 103 6.5 Linear Rank Tests 105 6.6 Variance of the Linear Rank Test 108 6.7 Optimal Rank Scores 110 6.8 Exact and Large-Sample Randomization Tests 111 6.8.1 Computation of exact tests 112 6.8.2 Large sample randomization tests 113 6.9 Monte Carlo Re-Randomization Tests 115 6.9.1 Unconditional tests 115 6.9.2 Example 116 6.9.3 Conditional tests 117 6.10 Preservation of Error Rates 118 6.11 Regression Modeling 120 6.12 Analyses with Missing Data 121 6.13 Sample Size Considerations for Random Sample Fractions 122 6.14 Group Sequential Monitoring 123 6.14.1 Establishing a stopping boundary 124 6.14.2 Information fraction 125 6.15 Problems 126 6.16 References 127 6.17 Appendix A 129 6.18 Appendix B 131 7 Stratification 133 7.1 Introduction 133 7.2 Stratified Randomization 134 7.3 Is Stratification Necessary? 135 7.4 Treatment Imbalances in Stratified Trials 136 7.5 Stratified Analysis Using Randomization Tests 138 7.6 Efficiency of Stratified Randomization in a Stratified Analysis 140 7.7 Conclusions 144 7.8 Problems 144 7.9 References 145 8 Restricted Randomization in Practice 147 8.1 Introduction 147 8.2 Stratification 148 8.3 Characteristics of Randomization Procedures 149 8.3.1 Consideration of selection bias 149 8.3.2 Implications for analysis 151 8.4 Selecting a Randomization Procedure 151 8.4.1 Choosing parameter values 152 8.4.2 Comparing procedures 153 8.4.3 Conclusions 156 8.5 Generation of Sequences 156 8.6 Implementation 157 8.6.1 Packaging and labeling 158 8.6.2 The actual randomization 159 8.7 Special Situations 160 8.8 Some Examples 163 8.8.1 The optic neuritis treatment trial 163 8.8.2 Vesnarinone in congestive heart failure 163 8.8.3 The diabetes control and complications trial 163 8.8.4 Captopril in diabetic nephropathy 164 8.8.5 The diabetes prevention program 164 8.8.6 Scleral buckling versus primary vitrectomy in retinal detachment (The SPR Study) 164 8.9 Problems 165 8.10 References 166 9 Covariate-Adaptive Randomization 169 9.1 Early Work 169 9.1.1 Zelen’s rule 170 9.1.2 The Pocock–Simon procedure 170 9.1.3 Example: Adjuvant chemotherapy for locally invasive bladder cancer 171 9.1.4 Wei’s marginal urn design 171 9.1.5 Is marginal balance sufficient? 171 9.1.6 Is randomization necessary? 172 9.2 More Recent Covariate-Adaptive Randomization Procedures 173 9.2.1 Balancing within strata 173 9.2.2 Balancing with respect to continuous covariates 174 9.3 Optimal Design Based on a Linear Model 175 9.4 The Trade-Off Among Balance, Efficiency, and Ethics 177 9.5 Inference for Covariate-Adaptive Randomization 179 9.5.1 Model-based inference 179 9.5.2 Randomization-based inference 180 9.6 Conclusions 181 9.7 Problems 182 9.8 References 185 10 Response-Adaptive Randomization 189 10.1 Introduction 189 10.2 Historical Notes 190 10.2.1 Roots in bandit problems 190 10.2.2 Roots in sequential stopping problems 191 10.2.3 Roots in randomization 192 10.3 Optimal Allocation 193 10.4 Response-Adaptive Randomization to Target R∗ 196 10.4.1 Sequential maximum likelihood procedure 196 10.4.2 Doubly adaptive biased coin design 198 10.4.3 Example 200 10.4.4 Efficient randomized-adaptive design 201 10.5 Urn Models 201 10.5.1 The generalized Friedman’s urn model 201 10.5.2 The randomized play-the-winner rule 202 10.5.3 Designs to target any allocation 205 10.5.4 Ternary urn models 206 10.5.5 Klein urn 207 10.6 Treatment Effect Mappings 207 10.7 Covariate-Adjusted Response-Adaptive Randomization 208 10.8 Problems 209 10.9 References 211 10.10 Appendix 214 11 Inference for Response-Adaptive Randomization 217 11.1 Introduction 217 11.2 Population-Based Inference 217 11.2.1 The likelihood 217 11.2.2 Sufficiency 220 11.2.3 Bias of the maximum likelihood estimators 220 11.2.4 Confidence interval procedures 222 11.3 Power 223 11.3.1 The relationship between power and the variability of the design 223 11.3.2 Asymptotically best procedures 225 11.3.3 Response-adaptive randomization and sequential monitoring 226 11.4 Randomization-Based Inference 226 11.5 Problems 228 11.6 References 228 12 Response-Adaptive Randomization in Practice 231 12.1 Basic Assumptions 231 12.2 Bias, Masking, and Consent 232 12.3 Logistical Issues 233 12.4 Selection of A Procedure 234 12.5 Benefits of Response-Adaptive Randomization 236 12.6 Some Examples 237 12.6.1 The extracorporeal membrane oxygenation trial 237 12.6.2 The fluoxetine trial 238 12.7 Conclusions 239 12.8 Problems 240 12.9 References 240 Author Index 243 Subject Index 249

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Book SynopsisThis book focuses on educating radiologists, radiation oncologists and others interested in imaging research about how to design and conduct clinical trials to evaluate imaging technology and imaging biomarkers.Table of ContentsContributors, vi Chapter 1 Imaging technology assessment, 1Pari V. Pandharipande and G. Scott Gazelle Chapter 2 Clinical trials of therapy, 10Sayeh Lavasani, Anthony F. Shields and Ali Mahinbakht Chapter 3 Clinical trials of image]guided interventions including radiotherapy studies, 29Gary S. Dorfman and Stephen M. Hahn Chapter 4 Imaging as a predictor of therapeutic response, 57David A. Mankoff and Anthony F. Shields Chapter 5 Screening trials and design, 76Janie M. Lee, Constance D. Lehman and Diana L. Miglioretti Chapter 6 Practicalities of running a clinical trial, 91Michael T. Lu, Elizabeth C. Adami and Udo Hoffmann Chapter 7 Statistical issues in study design, 103Nancy A. Obuchowski Chapter 8 Introduction to biostatistical methods, 126Diana L. Miglioretti, Todd A. Alonzo and Nancy A. Obuchowski Chapter 9 Methods for studies of diagnostic tests, 147Jeffrey D. Blume Chapter 10 Methods for quantitative imaging biomarker studies, 170Alicia Y. Toledano and Nancy A. Obuchowski Chapter 11 Introduction to cost]effectiveness analysis in clinical trials, 189Ruth C. Carlos and G. Scott Gazelle Index, 208

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Book SynopsisThe third edition of this best-selling book continues to offer a user-friendly, step-by-step introduction to all the key processes involved in bringing a drug to the market, including the performance of pre-clinical studies, the conduct of human clinical trials, regulatory controls, and even the manufacturing processes for pharmaceutical products. Concise and easy to read, Drugs: From Discovery to Approval, Third Edition quickly introduces basic concepts, then moves on to discuss target selection and the drug discovery process for both small and large molecular drugs. The third edition incorporates the latest developments and updates in the pharmaceutical community, provides more comprehensive coverage of topics, and includes more materials and case studies suited to college and university use. Biotechnology is a dynamic field with changes across R&D, clinical trials, manufacturing and regulatory processes, and the third edition of the text provides timely updates for thoTable of ContentsPreface xv 1 Introduction 1 1.1 Aim of this Book 1 1.2 An Overview of the Drug Discovery to Approval Process 2 1.3 The Pharmaceutical Industry 6 1.4 Economics of Drug Discovery and Development 11 1.5 Trends in Drug Discovery and Development 13 1.6 Case Study #1.1 15 1.7 Case Study #1.2 17 1.8 Summary of Important Points 20 1.9 Review Questions 20 1.10 Brief Answers and Explanations 21 1.11 Further Reading 22 2 Drug Discovery: Targets and Receptors 23 2.1 Drug Discovery Processes 23 2.2 Medical Needs 24 2.3 Target Identification 26content 2.4 Target Validation 33 2.5 Drug Interactions with Targets or Receptors 36 2.6 Enzymes 40 2.7 Receptors and Signal Transduction 42 2.8 Assay Development 52 2.9 Case Study #2.1 52 2.10 Case Study #2.2 53 2.11 Summary of Important Points 57 2.12 Review Questions 57 2.13 Brief Answers and Explanations 58 2.14 Further Reading 58 3 Drug Discovery: Small Molecule Drugs 61 3.1 Introduction 61 3.2 Irrational Approach 62 3.3 Rational Approach 67 3.4 Antisense Approach 85 3.5 RNA Interference Approach 88 3.6 Chiral Drugs 91 3.7 Closing Remarks 92 3.8 Case Study #3.1 94 3.9 Case Study #3.2 96 3.10 Summary of Important Points 98 3.11 Review Questions 99 3.12 Brief Answers and Explanations 99 3.13 Further Reading 100 4 Drug Discovery: Large Molecule Drugs 103 4.1 Introduction 103 4.2 Vaccines 105 4.3 Antibodies 117 4.4 Cytokines 128 4.5 Hormones 134 4.6 Gene Therapy 137 4.7 Stem Cells and Cell Therapy 139 4.8 Case Study #4.1 141 4.9 Case Study #4.2 144 4.10 Summary of Important Points 146 4.11 Review Questions 147 4.12 Brief Answers and Explanations 148 4.13 Further Reading 148 5 Drug Development and Preclinical Studies 151 5.1 Introduction 151 5.2 Pharmacodynamics 154 5.3 Pharmacokinetics 158 5.4 Toxicology 168 5.5 Animal Tests, In Vitro Assays, and In Silico Methods 172 5.6 Formulations and Delivery Systems 175 5.7 Nanotechnology 183 5.8 Case Study #5.1 184 5.9 Case Study #5.2 185 5.10 Summary of Important Points 187 5.11 Review Questions 188 5.12 Brief Answers and Explanations 188 5.13 Further Reading 189 6 Clinical Trials 191 6.1 Definition of Clinical Trial 191 6.2 Ethical Considerations 192 6.3 Clinical Trials 195 6.4 Regulatory Requirements for Clinical Trials 204 6.5 Clinical Data Management 215 6.6 Role of Regulatory Authorities 218 6.7 Gene Therapy Clinical Trial 218 6.8 Adaptive Clinical Trial 220 6.9 Meta-Analysis 221 6.10 Case Study #6.1 222 6.11 Case Study #6.2 226 6.12 Summary of Important Points 227 6.13 Review Questions 228 6.14 Brief Answers and Explanations 228 6.15 Further Reading 229 7 Regulatory Authorities 231 7.1 Role of Regulatory Authorities 231 7.2 US Food and Drug Administration 233 7.3 European Medicines Agency 236 7.4 Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) 238 7.5 China Food and Drug Administration 240 7.6 India’s Central Drugs Standard Control Organization 240 7.7 Australia’s Therapeutic Goods Administration 241 7.8 Canada’s Health Canada 243 7.9 Other Regulatory Authorities 243 7.10 Authorities other than Drug Regulatory Agencies 243 7.11 International Conference on Harmonization 244 7.12 World Health Organization 245 7.13 Pharmaceutical Inspection Cooperation Scheme 246 7.14 Case Study # 7.1 246 7.15 Case Study # 7.2 249 7.16 Summary of Important Points 250 7.17 Review Questions 251 7.18 Brief Answers and Explanations 251 7.19 Further Reading 252 8 Regulatory Applications 253 8.1 Introduction 253 8.2 United States 254 8.3 European Union 272 8.4 Japan 280 8.5 China 282 8.6 India 287 8.7 Australia 287 8.8 Canada 287 8.9 Case Study #8.1 290 8.10 Case Study #8.2 292 8.11 Summary of Important Points 294 8.12 Review Questions 299 8.13 Brief Answers and Explanations 299 8.14 Further Reading 300 9 Good Manufacturing Practice: Regulatory Requirements 301 9.1 Introduction 301 9.2 United States 302 9.3 Europe 308 9.4 International Conference on Harmonization (ICH) 309 9.5 Pharmaceutical Inspection Cooperation Scheme (PIC/S) 311 9.6 Selected Core Elements of GMP 312 9.7 Selected GMP Systems 335 9.8 New cGMP Initiatives 350 9.9 Case Study #9.1 352 9.10 Case Study #9.2 358 9.11 Summary of Important Points 362 9.12 Review Questions 363 9.13 Brief Answers and Explanations 363 9.14 Further Reading 364 10 Good Manufacturing Practice: Drug Manufacturing 367 10.1 Introduction 367 10.2 GMP Manufacturing 371 10.3 GMP Inspection 372 10.4 Manufacture of Small Molecule APIs (Chemical Synthesis Methods) 379 10.5 Manufacture of Large Molecule APIs (Recombinant DNA Methods) 385 10.6 Finished Dosage Forms 394 10.7 Product Quality Review 398 10.8 Manufacturing Variations 399 10.9 Case Study #10.1 400 10.10 Case Study #10.2 404 10.11 Summary of Important Points 407 10.12 Review Questions 408 10.13 Brief Answers and Explanations 408 10.14 Further Reading 408 11 Future Perspectives 411 11.1 Past Advances and Future Challenges 411 11.2 Small Molecule Pharmaceutical Drugs 412 11.3 Large Molecule Biopharmaceutical Drugs 414 11.4 Traditional Medicine 414 11.5 Personalized Medicine 419 11.6 Gene Therapy 420 11.7 Cloning and Stem Cells 420 11.8 Old Age Diseases and Aging 423 11.9 Lifestyle Drugs 423 11.10 Performance-Enhancing Drugs 428 11.11 Chemical and Biological Terrorism 428 11.12 Transgenic Animals and Plants 432 11.13 Antibiotics Drug Resistance 433 11.14 Regulatory Issues 435 11.15 Intellectual Property Rights and Marketing Exclusivities 437 11.16 Bioethics 440 11.17 Concluding Remarks 442 11.18 Case Study #11.1 445 11.19 Case Study #11.2 447 11.20 Further Reading 449 Appendix 1 History of Drug Discovery and Development 451 A1.1 Early History of Medicine 451 A1.2 Drug Discovery and Development in the Middle Ages 453 A1.3 Foundation of Current Drug Discovery and Development 454 A1.4 Beginnings of Modern Pharmaceutical Industry 454 A1.5 Evolution of Drug Products 455 A1.6 Further Reading 456 Appendix 2 Cells, Nucleic Acids, Genes, and Proteins 457 A2.1 Cells 457 A2.2 Nucleic Acids 460 A2.3 Genes and Proteins 462 A2.4 Further Reading 468 Appendix 3 Selected Drugs and Their Mechanisms Of Action 469 Appendix 4 A DHFR Plasmid Vector 481 Appendix 5 Vaccine Production Methods 483 Appendix 6 Vaccines Approved By FDA 485 Appendix 7 Pharmacology/Toxicology Review Format 489 Appendix 8 Examples of General Biomarkers 495 Appendix 9 Toxicity Grading 499 Appendix 10 Health Systems in Selected Countries 505 Acronyms 509 Glossary 515 Index 519

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Book SynopsisA comprehensive and practical resource for analyses of crossover designs For ethical reasons, it isvital to keep the number of patients in a clinical trial aslow as possible.As evidenced by extensive research publications, crossover designcan bea useful and powerfultool to reduce the number of patients needed for a parallel group design in studying treatmentsfor non-curable chronic diseases. This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. SystematTable of ContentsAbout the author xi Preface xii About the companion website xiv 1 Crossover design – definitions, notes, and limitations 1 1.1 Unsuitability for acute or most infectious diseases 2 1.2 Inappropriateness for treatments with long-lasting effects 2 1.3 Loss of efficiency in the presence of carry-over effects 3 1.4 Concerns of treatment-by-period interaction 3 1.5 Flaw of the commonly used two-stage test procedure 4 1.6 Higher risk of dropping out or being lost to follow-up 4 1.7 More assumptions needed in use of a crossover design 5 1.8 General principle and conditional approach used in the book 5 2 AB/BA design in continuous data 7 2.1 Testing non-equality of treatments 10 2.2 Testing non-inferiority of an experimental treatment to an active control treatment 11 2.3 Testing equivalence between an experimental treatment and an active control treatment 12 2.4 Interval estimation of the mean difference 13 2.5 Sample size determination 16 2.5.1 Sample size for testing non-equality 16 2.5.2 Sample size for testing non-inferiority 17 2.5.3 Sample size for testing equivalence 18 2.6 Hypothesis testing and estimation for the period effect 19 2.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 21 2.8 Examples of SAS programs and results 22 Exercises 27 3 AB/BA design in dichotomous data 30 3.1 Testing non-equality of treatments 34 3.2 Testing non-inferiority of an experimental treatment to an active control treatment 36 3.3 Testing equivalence between an experimental treatment and an active control treatment 39 3.4 Interval estimation of the odds ratio 40 3.5 Sample size determination 42 3.5.1 Sample size for testing non-equality 42 3.5.2 Sample size for testing non-inferiority 42 3.5.3 Sample size for testing equivalence 43 3.6 Hypothesis testing and estimation for the period effect 45 3.7 Testing and estimation for carry-over effects 47 3.8 SAS program codes and likelihood-based approach 48 Exercises 51 4 AB/BA design in ordinal data 57 4.1 Testing non-equality of treatments 62 4.2 Testing non-inferiority of an experimental treatment to an active control treatment 64 4.3 Testing equivalence between an experimental treatment and an active control treatment 65 4.4 Interval estimation of the generalized odds ratio 66 4.5 Sample size determination 67 4.5.1 Sample size for testing non-equality 67 4.5.2 Sample size for testing non-inferiority 68 4.5.3 Sample size for testing equivalence 68 4.6 Hypothesis testing and estimation for the period effect 70 4.7 SAS codes for the proportional odds model with normal random effects 72 Exercises 74 5 AB/BA design in frequency data 75 5.1 Testing non-equality of treatments 78 5.2 Testing non-inferiority of an experimental treatment to an active control treatment 81 5.3 Testing equivalence between an experimental treatment and an active control treatment 83 5.4 Interval estimation of the ratio of mean frequencies 84 5.5 Sample size determination 86 5.5.1 Sample size for testing non-equality 86 5.5.2 Sample size for testing non-inferiority 87 5.5.3 Sample size for testing equivalence 88 5.6 Hypothesis testing and estimation for the period effect 88 5.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 90 Exercises 92 6 Three-treatment three-period crossover design in continuous data 95 6.1 Testing non-equality between treatments and placebo 102 6.2 Testing non-inferiority of an experimental treatment to an active control treatment 103 6.3 Testing equivalence between an experimental treatment and an active control treatment 104 6.4 Interval estimation of the mean difference 104 6.5 Hypothesis testing and estimation for period effects 105 6.6 Procedures for testing treatment-by-period interactions 107 6.7 SAS program codes and results for constant variance 109 Exercises 111 7 Three-treatment three-period crossover design in dichotomous data 115 7.1 Testing non-equality of treatments 121 7.1.1 Asymptotic test procedures 121 7.1.2 Exact test procedures 123 7.1.3 Procedures for simultaneously testing non-equality of two experimental treatments versus a placebo 124 7.2 Testing non-inferiority of an experimental treatment to an active control treatment 126 7.3 Testing equivalence between an experimental treatment and an active control treatment 127 7.4 Interval estimation of the odds ratio 129 7.5 Hypothesis testing and estimation for period effects 131 7.6 Procedures for testing treatment-by-period interactions 133 7.7 SAS program codes and results for a logistic regression model with normal random effects 136 Exercises 138 8 Three-treatment three-period crossover design in ordinal data 141 8.1 Testing non-equality of treatments 150 8.1.1 Asymptotic test procedures 150 8.1.2 Exact test procedure 152 8.2 Testing non-inferiority of an experimental treatment to an active control treatment 153 8.3 Testing equivalence between an experimental treatment and an active control treatment 153 8.4 Interval estimation of the GOR 154 8.5 Hypothesis testing and estimation for period effects 156 8.6 Procedures for testing treatment-by-period interactions 159 8.7 SAS program codes and results for the proportional odds model with normal random effects 160 Exercises 162 9 Three-treatment three-period crossover design in frequency data 164 9.1 Testing non-equality between treatments and placebo 170 9.2 Testing non-inferiority of an experimental treatment to an active control treatment 173 9.3 Testing equivalence between an experimental treatment and an active control treatment 174 9.4 Interval estimation of the ratio of mean frequencies 175 9.5 Hypothesis testing and estimation for period effects 178 9.6 Procedures for testing treatment-by-period interactions 179 Exercises 181 10 Three-treatment (incomplete block) crossover design in continuous and dichotomous data 183 10.1 Continuous data 185 10.1.1 Testing non-equality of treatments 188 10.1.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 189 10.1.3 Interval estimation of the mean difference 190 10.1.4 SAS codes for fixed effects and mixed effects models 192 10.2 Dichotomous data 194 10.2.1 Testing non-equality of treatments 197 10.2.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 199 10.2.3 Testing non-inferiority of either experimental treatment to an active control treatment 199 10.2.4 Interval estimation of the odds ratio 200 10.2.5 SAS codes for the likelihood-based approach 202 Exercises 203 References 208 Index 216

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Book SynopsisStatistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned, mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ' principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V' principle provides a simple estimator of the log odds ratio that is ideally suited for stratified

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Book SynopsisA Practical Guide to Managing Clinical Trials is a basic, comprehensive guide to conducting clinical trials. Designed for individuals working in research site operations, this user-friendly reference guides the reader through each step of the clinical trial process from site selection, to site set-up, subject recruitment, study visits, and to study close-out. Topics include staff roles/responsibilities/training, budget and contract review and management, subject study visits, data and document management, event reporting, research ethics, audits and inspections, consent processes, IRB, FDA regulations, and good clinical practices. Each chapter concludes with a review of key points and knowledge application.Unique to this book is A View from India, a chapter-by-chapter comparison of clinical trial practices in India versus the U.S. Throughout the book and in Chapter 10, readers will glimpse some of the challenges and opportunities in the emerging and Trade Review'"A Practical Guide to Managing Clinical Trials" provides a good introduction to the basics of clinical research for investigators, study coordinators, and other site personnel. The clear writing makes the book a quick read.' – Norman M. Goldfarb for Journal of Clinical Research Best Practices, Vol. 13, No. 12, December 2017.'"A Practical Guide to Managing Clinical Trials" provides a good introduction to the basics of clinical research for investigators, study coordinators, and other site personnel. The clear writing makes the book a quick read.' – Norman M. Goldfarb for Journal of Clinical Research Best Practices, Vol. 13, No. 12, December 2017.Table of ContentsChapter 1: Rules, Roles and ResponsibilitiesChapter 2: Products, Protocols, and Pre-trial PreparationChapter 3: Sponsor, Site and Study Start-upChapter 4: Enticement, Enrollment, and Engagement: The Informed Consent ProcessChapter 5: From Enrollment to Final VisitChapter 6: Collaborating for Compliance and Quality Data – Monitoring and AuditsChapter 7: Building BudgetsChapter 8: Contracts, Clauses and Closing the DealChapter 9: US Clinical Trials – Additional TopicsChapter 10: Clinical Research and India

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Book SynopsisSample Sizes for Clinical Trials, Second Edition is a practical book that assists researchers in their estimation of the sample size for clinical trials. Throughout the book there are detailed worked examples to illustrate both how to do the calculations and how to present them to colleagues or in protocols. The book also highlights some of the pitfalls in calculations as well as the key steps that lead to the final sample size calculation.Features: Comprehensive coverage of sample size calculations, including Normal, binary, ordinal, and survival outcome data Covers superiority, equivalence, non-inferiority, bioequivalence and precision objectives for both parallel group and crossover designs Highlights how trial objectives impact the study design with respect to both the derivation of sample formulae and the size of the study Motivated with examples of real-life clinical trials showing how the calculationsTable of Contents1. Introduction. 2.Seven Key Steps to Cook up a Sample Size. 3. Sample Sizes for Parallel Group Superiority Trials with Normal Data. 4. Sample Size Calculations for Superiority Cross-Over Trials with Normal Data. 5. Sample Size for Cluster Randomized Trials. 6. Allowing for Multiplicity in Sample Sizes Calculations for Clinical Trials. 7. Samples Size Calculations for Non-Inferiority Clinical Trials with Normal Data. 8. Sample Size Calculations for Equivalence Clinical Trials with Normal Data. 9. Sample Size Calculations for Bioequivalence Trials. 1. Sample Size Calculations for Precision Clinical Trials with Normal Data. Sample Size for Pilot Studies. 12. Sample Size Calculations for Parallel Group Superiority Clinical Trials with Binary Data. 13. Sample Size Calculations for Superiority Cross-Over Clinical Trials with Binary Data. 14. Sample Size Calculations for Non-Inferiority Trials with Binary Data. 15. Sample Size Calculations for Equivalence Trials with Binary Data. 16. Sample Size Calculations for Precision Trials with Binary Data. 17. Sample Size Calculations for Single Arm Clinical Trials and Studies with Finite Population Size. 18. Sample Sizes for Clinical Trials with an Adaptive Design. 19. Sample Size Calculations for Clinical Trials with Ordinal Data. 20. Estimating the Number of Events for Clinical Trials with Survival Data for a Negative Outcome. 21. Sample Size Calculations for Clinical Trials with Survival Data and a Positive Outcome. 22. Sample Size Calculations for Clinical Trials with Survival Data Allowing for Recruitment and Withdrawal. 23. Appendix. 24. References.

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Book SynopsisPower and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software. It is designed to be used as a learning tool for students as well as a resource for experienced statisticians and investigators.The book begins by explaining the process of power calculation step by step at an introductory level and then builds to increasingly complex and varied topics. For each type of study design, the information needed to perform a calculation and the factors that affect power are explained. Concepts are explained with statistical rigor but made accessible through intuition and examples. Practical advice for performing sample size and power calculations for real studies is given throughout.The book demonstrates calculations in R. It is integrated with the companion R package powertools and also draws on and summarizes the capabilities of other R packages.

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Book SynopsisComprehensive resource presenting methods essential in planning, designing, conducting, analyzing, and interpreting clinical trials The Fourth Edition of Clinical Trials builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials, discussing principles of design for various types of clinical trials and elements of planning the experiment, assembling a study cohort, assessing data, and reporting results. Each chapter contains an introduction and summary to reinforce key points. Discussion questions stimulate critical thinking and help readers understand how they can apply their newfound knowledge. Written by a highly qualified author with significant experience in the field, the Fourth Edition of Clinical Trials approaches the topic with: Problems that may arise during a trial, and accompanying common sense solutionsDesign alternatives for addressing many questions in therapeutic developmentStatistical principles with new and provocative topics, such as generalizing results, operating characteristics, trial issues during the COVID-19 pandemic, and moreAlternative medicine, ethics, middle development, comparative studies, adaptive designs, and clinical trials using point of care dataRevamped exercise sets, updated and extensive references, new material on endpoints and the developmental pipeline, and revisions of numerous sections, tables, and figures Standing out due to its accessible and broad coverage of statistical design methods which are the building blocks of clinical trials and medical research, Clinical Trials is an essential learning aid on the subject for undergraduate and graduate clinical trials courses.

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Book SynopsisThe pace of therapeutic advances in the treatment of cardiovascular diseases is rapid, and new clinically-relevant information appears with such frequency that it can be extremely challenging for clinicians to keep up. Still, knowledge and interpretation of major clinical trials is crucial for the range of clinicians who manage cardiovascular patients, especially since important trial evidence often needs to be implemented soon after it is published. Confidently apply gold standard treatment for 10 of the most critical areas of cardiology Written by an international team of experts, Cardiovascular Clinical Trials: Putting the Evidence into Practice: Provides a succinct overview of recent major clinical trials - the gold standard for all medical treatment - across all the major cardiovascular subspecialties, to ensure you're up-to-date on the most critical findings Guides cardiology trainees and clinicians on how cardiovTrade Review“This is an excellent contribution to the evidence-based practice of cardiovascular medicine and will be a valuable reference tool.” (Doody’s, 14 June 2013) Table of ContentsList of Contributors viii Preface x List of Abbreviations xi 1 Introduction to randomized clinical trials in cardiovascular disease 1 Tobias Geisler, Marcus D. Flather, Deepak L. Bhatt, and Ralph B. D’Agostino, Sr 2 Publishing results of clinical trials and reviewing papers for publication 44 Tobias Geisler and Marcus D. Flather 3 Management of chronic coronary artery disease 60 Sabu Thomas and William E. Boden 4 Acute coronary syndromes (ST elevation and non-ST elevation) 86 Tobias Geisler, Deepak L. Bhatt, and Marcus D. Flather 5 Heart failure 117 Christopher M. O’Connor and Wendy Gattis Stough 6 Atrial fibrillation 143 Chee W. Khoo and Gregory Y.H. Lip 7 Electrophysiology and pacing 174 Irina Suman-Horduna and Sabine Ernst 8 Percutaneous coronary intervention 205 Dharam J. Kumbhani and Deepak L. Bhatt 9 Randomized controlled trials in cardiac surgery: is there any alternative? 243 Thanos Athanasiou, Amir Sepehripour, and John Pepper 10 Adult congenital heart disease 274 Cary Ward, J. Kevin Harrison, and Thomas M. Bashore 11 Cardiac imaging 296 Aiden Abidov and Daniel S. Berman 12 Prevention of cardiovascular disease 345 Alice J. Owen and Christopher M. Reid Index 378

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Book SynopsisThe publication of the second edition of this manual comes at an important juncture in the history of clinical research. As advances in information technology make it possible to link individuals and groups in diverse locations in jointly seeking the answers to pressing global health problems, it is critically important to remain vigilant about moral and ethical safeguards for every patient enrolled in a trial. Those who study this manual will be well aware of how to ensure patient safety along with fiscal responsibility, trial efficiency, and research integrity. Robert Harrington, Professor of Medicine, Director, Duke Clinical Research Institute, Durham, North Carolina, USA The Duke Clinical Research Institute (DCRI) is one of the world''s leading academic clinical research organizations; its mission is to develop and share knowledge that improves the care of patients around the world through innovative clinical research. This concise handbook provides a practical nutTrade Review"This is an excellent guide to how to conduct clinical trials of medical devices and biologics in the light of recent regulatory and clinical developments. The roles and responsibilities of institutional review boards and recent developments regarding subject privacy concerns and regulations are well covered. This manual also provides sample forms and worksheets for data management." (Doody's, 5 August 2011) "A Clinical Trials Manual from the Duke Clinical Research Institute: Lessons from a Horse Named Jim, 2nd Edition" is a good introduction to clinical research, primarily from the site perspective. The book gives readers a solid foundation of principles and knowledge." (Journal of Clinical Research Best Practices, 8 August 2011) Table of ContentsForeword by Robert A. Harrington xiii Preface xv List of Abbreviations xviii 1 Lessons from a Horse Named Jim and Other Events in History Affecting the Regulation of Clinical Research 1 2 The Process: Developing New Drugs, Biologics, and Devices 13 The Drug Development Process 14 Background Information 14 Pre-Clinical Studies 15 The Investigational New Drug Application 16 Clinical Trial Phases 17 Application to Market New Drugs and Biologics 20 FDA Review Groups 21 Early or Expanded Access to Unapproved Drugs and Biologics 24 Orphan Drugs 25 Developing New Devices 26 Background Information 27 What is a Medical Device? 28 Medical Device Classification 29 Requirements for Marketing New Devices 33 Humanitarian Use Devices 36 Early or Expanded Access to Unapproved Medical Devices 36 FDA Device Review 38 Combination Products 38 Postmarketing Surveillance of Drugs, Biologics, and Devices 39 Phase 4 Postmarketing Drug and Biologics Studies 40 Phase 4 Postmarketing Device Studies 40 Direct Reporting Based on Observations 41 3 Good Clinical Practice and the Regulations 49 Good Clinical Practice 50 Regulations 50 Guidelines 59 Local Laws 60 Responsibilities in the Code of Federal Regulations 62 Principal Investigator Responsibilities 62 Institutional Review Board Responsibilities 67 Sponsor Responsibilities 68 Sponsor-Investigators 70 Where to Obtain Information and Guidance for the Regulations and GCP 70 The Federal Register 70 FDA Guidance Documents 71 Online Resources 71 4 Informed Consent and the Regulations 73 What Is Informed Consent? 74 Ethical Codes Regarding Informed Consent 75 The Belmont Report: Application of Respect for Persons 75 The Declaration of Helsinki 76 The Nuremberg Code 77 Regulatory Requirements for Informed Consent 77 General Requirements for Informed Consent (21 CFR 50.20) 78 Exceptions from the General Requirements (21 CFR 50.23) 79 Exceptions from Informed Consent Requirements for Emergency Research (21 CFR 50.24) 79 Elements of Informed Consent (21 CFR 50.25) 80 Documentation of Informed Consent (21 CFR 50.27) 82 Consent from Vulnerable Subjects 85 HIPAA/Privacy Rule Requirements 90 The Informed Consent Process 92 Writing the Consent Form 92 Obtaining Informed Consent 95 Documenting Informed Consent 96 Continuing Informed Consent 97 5 Institutional Review Boards 101 What is an Institutional Review Board? 102 Types of IRBs 103 IRB Membership 104 IRB Activities 107 Reviewing Research 107 Reporting Unanticipated Problems Involving Risks to Subjects or Others 109 Establishing Written Procedures 110 Types of IRB Review 111 Full Committee Review 111 Expedited Review 112 Items That Must be Submitted for IRB Review 113 Exemptions: When IRB Approval Is Not Required 113 Continuing Review after Initial Study Approval 114 Review of Adverse Events and Unanticipated Problems 115 Communication between IRBs and Investigators 116 Investigator Notification of the Outcome of IRB Review 116 Communication During Study 116 IRB Notification at Study Completion 117 Communication between IRBs and Study Sponsors 117 IRB Records and Reports 118 Accreditation of IRBs 119 Registration 120 6 Adverse Events and Unanticipated Problems Involving Risks to Subjects or Others 123 Why Collect Adverse Event Data? 124 Safety Profile 125 Benefits and Risks Evaluation 125 Package Insert 125 Adverse Events 125 Internal and External Adverse Events 126 Serious Adverse Events 126 Unanticipated Problems Involving Risks to Subjects or Others 127 Investigator Responsibilities 129 Collecting Adverse Event Data 129 Reporting Adverse Event Data 130 Expedited Reporting of Adverse Events 131 Reporting Unanticipated Problems Involving Risks to Subjects or Others 133 Reporting Unanticipated Adverse Device Effects 135 IRB Responsibilities 135 Review and Reporting of Serious Adverse Events 135 Review and Reporting of Unanticipated Problems 136 Sponsor Responsibilities 136 Expedited Reporting in Drug Trials 137 Expedited Reporting in Device Trials 138 Routine Reporting by Sponsors 139 7 Monitoring, Audits, and Inspections 141 Monitoring Plan 143 On-Site Monitoring 144 Types of On-Site Monitoring Visits 145 Documenting Monitoring Visits 151 In-House Monitoring 152 Computerized Checks 153 Source Document Verification Done at the Sponsor or Data Center 153 Protected Health Information 154 Audits and Inspections 154 Audits and Inspections in the Regulations and Guidelines 155 Sponsor Quality Assurance Audits 156 FDA Inspections 157 8 The Principal Investigator, the Clinical Research Coordinator, and the Study Site 163 The Principal Investigator 164 Characteristics of an Effective Principal Investigator 165 Conflict of Interest 167 Investigator Delegation of Study Activities 169 Staffing to Support Clinical Trials 169 Clinical Research Coordinator 169 Subinvestigators 172 Support Personnel 173 Space and Resource Needs 173 Workspace for the Clinical Research Coordinator 173 Equipment 174 Storage Space 174 Additional Space 175 The Local Institutional Review Board 175 9 The Protocol 177 Common Components of a Protocol 180 Background and Rationale 180 Study Organization 180 Objectives/Endpoints 181 Quality of Life Parameters 181 Economic Factors 182 Surrogate Endpoints 182 Study Design 183 Use of Control Groups 184 Randomization 185 Blinding 187 Observational Studies 188 Study Population 190 Study Treatment Plan 191 Safety Assessment, Management, and Reporting 192 Replacement of Withdrawn, Dropped Out, and Lost to Follow-up Subjects 193 Statistical Aspects 193 Power 193 Sample Size 193 Intention-to-treat Principle 194 Interim Analysis 195 Data and Safety Monitoring Board 196 Subject Data and Record Retention 197 Monitoring 197 10 Study Feasibility: Reviewing a Specific Protocol 199 Reviewing a Specific Protocol 200 Study Design 200 Research Subject Population 201 Investigator Time Requirements 202 Clinical Research Coordinator and Other Study Personnel 202 Laboratory Tests and Procedures 204 Additional Space and Equipment 205 Budget Considerations 206 Preparing a Budget 207 Budget Planning 209 Negotiating a Budget 211 Should We Do this Study at Our Site? 211 11 Study Activities 213 Study Start-up Phase 215 Review the Protocol, Develop a Budget, Prepare Documents for IRB Submission 215 Establish the Site Study Team 216 Participate in Investigator Meetings 219 Develop a Recruitment and Enrollment Plan 219 Conduct Education and Training Sessions for Site Personnel 228 Begin Randomization and Enrollment of Subjects 230 Study Maintenance Phase 230 Complete Data Forms 231 Report Serious Adverse Events (SAE) and Unanticipated Problems 231 Conduct Subject Follow-up Visits 231 Ensure Subject Retention and Compliance 233 Unblind Study Treatment Only When Required 238 Maintain Study Drug/Device Accountability 239 Manage Specimens, Samples, and Other Study-related Materials 239 Obtain Answers to Urgent Clinical Questions 239 Continue Communication 239 Maintain Study File 240 Study Completion and Close-Out Phase 240 Completion of All Subject Data Forms and Resolution of Data Queries 241 Destruction or Return of Study Materials 241 Review of Site Study File 241 Submission of the Final Report 241 Long-term Storage of Study Records 242 12 Study Documents/Essential Documents 245 Documents at Study Start-Up 246 Confidentiality Agreement 247 Signed Protocol and Applicable Amendments 247 Letter of Agreement 247 Investigator’s Brochure 248 Curriculum Vitae (CV)/Statement of Investigator Qualifications 248 Medical Licensure Form 248 Form FDA 1572 248 Financial Disclosure Information 250 IRB Approval 250 IRB-Approved Consent Form 252 RB-Approved Advertisements and Subject Materials 253 Laboratory Certification and Normal Ranges Form 253 Site Demographics Form 255 Study Personnel CVs/Résumés and Training Records 255 Contractual Agreement/Financial Contract 255 Documents While the Study is in Progress 256 Protocol Amendments and IRB Approval 256 Revised Consent Forms and IRB Approval 257 Updated Form FDA 1572 257 CVs for New PIs and Subinvestigators 257 Updated Laboratory Certification and Normal Ranges Form 258 IRB Correspondence 258 Subject Recruitment Advertisements and Educational Materials 258 Screening Log 258 Confidential Master Subject Log 259 Signed Consent Forms for All Enrolled Subjects 259 Test Article Accountability Forms 259 Serious and Reportable Adverse Event Forms 259 Subject Data Forms and Query Forms 261 Source Documents 261 Signature and Delegation Log 263 Site Visit Log 263 Written Communication and Correspondence 263 Documents at Study Close-out 263 Outstanding Data Forms and Query Forms 264 Complete Sets of All Subject Data Forms 264 Final Reports 264 Test Article Accountability Records 264 Maintaining Your Site Study File 266 Record Retention 266 Principal Investigator Status Change 267 Final Financial Disclosure Report 267 Sample Study File Organization 267 13 Management of Study Drugs, Biologics, and Devices 271 Study Drugs and Biologics 272 Study Drug Accountability 272 Study Drug Packaging 273 Study Drug Receipt 274 Study Drug Storage 274 Dispensing Study Drug 274 Study Drug Unblinding 277 Final Disposition of Study Drug 278 Study Devices 278 Device Labeling 278 Device Accountability 279 Device Tracking 279 14 Managing Clinical Trial Data 281 HIPAA, the Privacy Rule, and Clinical Trial Data 282 Use of Protected Health Information With Individual Authorization 283 Use of Protected Health Information Without Individual Authorization 283 Subject Identifiers 284 Guidelines and Regulations Regarding Clinical Trial Data 284 ICH E6 Section 2: The Principles of ICH GCP 284 ICH E6 Section 4: Records and Reports 285 21 CFR 312 and §812 285 Electronic Data 285 Study Site Responsibilities Regarding Clinical Trial Data 287 Record the Data in Source Documents 287 Complete Data Forms 290 Correct the Data 302 Submit the Data 307 Store/Archive the Data 308 Source Document Verification of Clinical Trial Data 308 Release of Protected Medical Information 309 Confidentiality of Clinical Trial Data 310 Endpoint Adjudication 310 15 Global Health and International Trials 313 International Clinical Trials 314 Ethnic and Racial Differences 315 Ethical Issues and Cultural Sensitivities 316 Why International Trials Are Important 317 HIV/AIDS 318 Malaria 318 Tuberculosis 318 Polio 319 International Regulations 320 Concerns 321 Future Efforts 322 Appendices 325 Appendix A 327 Appendix B 342 Appendix C 355 Appendix D 362 Appendix E 370 Epilogue by Lisa G. Berdan 379 Glossary 382 Index 395

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Book SynopsisThis book provides a comprehensive resource for medical professionals on the various legal aspects involved in conducting clinical research.Trade Review “Clinical Research and the Law” provides thoughtful and practical information on a broad range of legal topics related to clinical research, with an emphasis on subject injury liability. The book is useful for anyone who is not an expert in a particular area of law, with numerous citations for further investigation.” (Journal of Clinical Research Best Practices, 1 October 2012) Table of ContentsPreface, ix Chapter 1: Research malpractice and negligence, 1 1.1 Background, 1 1.2 Drugs: brief description of definitions, 4 1.3 Brief overview: conduct of clinical trials, 5 1.4 Medical devices, 6 1.5 Research malpractice: the basics, 7 1.6 Negligence actions and research: interesting aspects of medical research negligence cases, 8 Chapter 2: Duty of care: understanding the legal differences between medical treatment and medical research, 23 2.1 Establishing duty of care, 23 2.2 Do sponsors have a legal duty?, 27 Chapter 3: Establishing standard of care and violation of standard of care, 33 3.1 Research malpractice and using expert testimony to establish the standard of care, 34 3.2 Lessons learned from surgical innovation cases, 35 3.3 Standard of care and informed consent cases, 36 Chapter 4: Informed consent in clinical research, 37 4.1 Basics on informed consent in the clinical treatment setting: background, 37 4.2 Informed consent as applied to the research setting, 40 4.3 Informed consent and federal regulations, 42 4.4 Case law and federal regulations, 43 4.5 Clinical trials and pediatric patients, 46 Chapter 5: Liability issues for institutional review boards (IRBs) and data safety monitoring boards (DSMBs), 55 5.1 Liability for negligence, 56 5.2 Standard of care, 57 5.3 Proximate cause and damages, 59 5.4 Defense, 60 5.5 Practical considerations: the need for indemnification, 62 5.6 Special considerations for DSMBs, 62 Chapter 6: Legal aspects of financial conflicts of interest in clinical trials, 65 6.1 Overview, 66 6.2 Legislative background: road to creating financial conflicts of interest, 68 6.3 Financial conflicts of interest: evidence that financial conflicts of interest are problematic, 69 6.4 Regulations/legislation, 70 6.5 Litigation involving financial conflicts of interest in clinical trials, 74 6.6 Applying novel legal theories to financial conflicts of interest cases, 79 6.7 Other clinical trial cases involving financial conflicts of interest claiming constitutional violations, 81 Chapter 7: Disclosure of clinical trial information: legal ramifications of withholding study results, 87 7.1 GlaxoSmithKline, 89 7.2 Vioxx and Merck, 91 7.3 Government and other clinical trial disclosure requirements, 97 7.4 Medical journal editors and disclosure of clinical trial information, 98 Chapter 8: Clinical trials and insider trading, 105 8.1 Purpose of insider trading laws, 105 8.2 Proving insider trading, 106 8.3 Penalties, 108 8.4 Insider trading cases and clinical trials, 108 8.5 Beware: investigators and relationships with the investment industry—a risk of recent vintage, 111 8.6 Setting the stage, 113 Chapter 9: Clinical trials and criminal law, 117 9.1 How clinical trial investigators have been implicated in criminal acts, 119 9.2 False Claims Act cases and health-care fraud, 120 9.3 Clinical trial False Claims Act cases, 122 9.4 Enforcement of the False Claims Act against institutions, 130 9.5 Anti-kickback law, 132 9.6 Health-care fraud, 138 9.7 Mail and wire fraud/making false statements to government officials, 141 9.8 Proposed new FDA rule, 143 Chapter 10: Clinical trial contracts, 145 10.1 Key terms/scope of study, 146 10.2 Costs/payments, 147 10.3 Data, 147 10.4 Intellectual property, 148 10.5 Indemnification/injuries, 148 10.6 Publications, 149 10.7 Various sundry provisions, 149 Appendix A: Glossary of common terms used in connection with clinical trials, 151 Appendix B: Research involving human subjects, 163 Appendix C: Best pharmaceuticals for Children Act, 173 Appendix D: Pediatric research Equity Act of 2003, 193 Appendix E: Title 21–food and drugs: additional safeguards for children in clinical investigations, 203 Appendix F: Proposed standardized/harmonized clauses for clinical trial agreements, 209 Appendix G: Responsibility of applicants for promoting objectivity in research for which public health service funding is sought and responsible prospective contractors, 225 Index, 257

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Book SynopsisHealthcare professionals who want to increase their chances of approval can depend on the practical advice found in this book by a highly experienced clinical trialist author. Providing a concise and easy-to-read overview, How to Write a Grant Application includes pointers on what to include and what to avoid.Trade Review"Intended for those who are preparing to apply for a clinical grant, this guide covers this complex process in an accessible manner." (Booknews, 1 April 2011)Table of ContentsForeword. Preface. Acknowledgements. About the author. Chapter 1: Overview. 1.1 Types of grants. 1.2 Types of funding organisations. 1.3 Choosing an appropriate funding body. 1.4 Contents of the grant application. 1.5 Including several studies in one application (project grants). 1.6 Translational research sub-studies. 1.7 The application process. 1.8 Estimating timelines and a planned work schedule. 1.9 Intellectual property. 1.10 Text, grammar and format. Chapter 2: People involved in the study. 2.1 Who should be part of the Study Team? 2.2 Other investigators, collaborators and consultants. 2.3 The host institution and Sponsor. 2.4 Commercial companies. 2.5 Oversight committees. Chapter 3: Justifi cation for the study. 3.1 Finding background information. 3.2 Previous evidence and similar research (why the study is needed now). 3.3 Biological plausibility. 3.4 Safety of new interventions in clinical trials. 3.5 Feasibility. 3.6 What will the study contribute? 3.7 Summary of the justifi cation for a proposed study. Chapter 4: Describing the study design. 4.1 Abstract. 4.2 Appendices. 4.3 Study objectives and outcome measures. 4.4 Types of studies. 4.5 Observational studies in humans. 4.6 Clinical trials in humans. 4.7 Laboratory experiments. 4.8 Describing sample size. 4.9 Describing the main statistical analyses. 4.10 Systematic reviews. Chapter 5: Associated documents with the grant application. 5.1 Study protocol. 5.2 Participant Information Sheet. 5.3 Curricula vitae of the Chief Investigator and all co-applicants. 5.4 Letters of support from co-applicants, centre investigators, collaborators, or other advisors. 5.5 Letters of support from commercial companies. 5.6 Other documents specifi c to the fi eld of research. Chapter 6: Financial costs. 6.1 Overview of items to include in the fi nancial costs. 6.2 Indirect costs or overheads (full economic costs). 6.3 Per patient (or per subject) payments. 6.4 Staff costs. 6.5 Access to core funds and resources. 6.6 Consideration of costs not to be met by the funding body. 6.7 Grant applications associated with calls for proposals. 6.8 Observational studies in humans. 6.9 Clinical trials in humans. 6.10 Laboratory experiments. 6.11 Systematic reviews. Chapter 7: Funding body review process. 7.1 Submitting the application. 7.2 Processing the application within the funding body. 7.3 Initial reviews (external reviewers). 7.4 Funding committee meeting. 7.5 Funding committee evaluation. 7.6 Feedback to applicants after the meeting. 7.7 Responding to the funding committee feedback. Chapter 8: Annual reports and applying for a grant extension. 8.1 Annual reports. 8.2 Applying for a grant extension. Bibliography. Index.

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Book SynopsisAdaptive Designs for Sequential Treatment Allocation presents a rigorous theoretical treatment of the results and mathematical foundation of adaptive design theory. The book focuses on designing sequential randomized experiments to compare two or more treatments incorporating information accrued along the way. The authors first introduce the terminology and statistical models most commonly used in comparative experiments. They then illustrate biased coin and urn designs that only take into account past treatment allocations as well as designs that use past data, such as sequential maximum likelihood and various types of doubly adaptive designs. The book also covers multipurpose adaptive experiments involving utilitarian choices and ethical issues. It ends with adaptive methods that include covariates in the design. The appendices present basic tools of optimal design theory and address Bayesian adaptive designs.Trade Review"I fully endorse views of the authors that researchers working in the area of adaptive designs may find this book a useful reference. Further, since this book includes a fair number of examples, teachers of graduate-level courses on designs may also find this book useful." -Sada Nand Dwivedi, International Society for Clinical Biostatistics "... the book illustrates theoretical properties of adaptive designs so that researchers can choose the best design for the experiment, covering impressively diverse approaches. ... I find the book quite appealing in that the authors believed on the theoretical properties of the designs and mathematical foundations more than how and what of adaptive designs ... highly useful as a reference book in a graduate-level course on designs with nearly exhaustive approaches to adaptive design construction." -Biometrics, December 2015Table of ContentsFundamentals and Preliminary Results. Randomized Procedures That Are Functions of the Past Allocations. Randomized Procedures That Depend on the Responses. Multipurpose Adaptive Designs: Step-by-Step Procedures. Multipurpose Adaptive Designs: Constrained and Combined Optimality. Randomized Procedures That Depend on the Covariates. Appendices. Bibliography. Index.

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Book SynopsisSince 1945, The Annual Deming Conference on Applied Statistics has been an important event in the statistics profession. In Clinical Trial Biostatistics and Biopharmaceutical Applications, prominent speakers from past Deming conferences present novel biostatistical methodologies in clinical trials as well as up-to-date biostatistical applications from the pharmaceutical industry.Divided into five sections, the book begins with emerging issues in clinical trial design and analysis, including the roles of modeling and simulation, the pros and cons of randomization procedures, the design of Phase II dose-ranging trials, thorough QT/QTc clinical trials, and assay sensitivity and the constancy assumption in noninferiority trials. The second section examines adaptive designs in drug development, discusses the consequences of group-sequential and adaptive designs, and illustrates group sequential design in R. The third section focuses on oncology clinical triTrade Review"This book of timely, self-contained chapters covers a wide range of current methods and unresolved challenges in clinical trial statistical methods and will prove to be an essential guide for biostatisticians who work in this field."—Dirk F. Moore, Journal of Biopharmaceutical Statistics". . . the contributed chapters in this collection are clearly written, easily digestible, and well-referenced. The book is a useful resource for the clinical trialist interested in obtaining a quick overview of standard practices and current methodological development for a specific biostatistical application, or a worthwhile read for the researcher seeking to familiarize him or herself with a diversity of emerging topics in clinical trials.—Megan T. Smith, University of California, IrvineTable of ContentsEmerging Issues in Clinical Trial Design and Analysis. Adaptive Clinical Trials. Clinical Trials in Oncology. Multiple Comparisons in Clinical Trials. Clinical Trials in the Genomic Era. Index.

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Book SynopsisCancer is a major healthcare burden across the world and impacts not only the people diagnosed with various cancers but also their families, carers, and healthcare systems. With advances in the diagnosis and treatment, more people are diagnosed early and receive treatments for a disease where few treatments options were previously available. As a result, the survival of patients with cancer has steadily improved and, in most cases, patients who are not cured may receive multiple lines of treatment, often with financial consequences for the patients, insurers and healthcare systems. Although many books exist that address economic evaluation, Economic Evaluation of Cancer Drugs using Clinical Trial and Real World Data is the first unified text that specifically addresses the economic evaluation of cancer drugs. The authors discuss how to perform cost-effectiveness analyses while emphasising the strategic importance of designing cost-effectiveness into cancer trialTrade Review"This book is highly recommended for readers searching for an introductory text to the world of health economic analysis. The authors provide timely examples from both clinical trials in oncology and subsequent real-world application, discussing implications of findings and how they could potentially be applied to make future trials and real-world applications more efficient. ~Nuru Noor, ISCB NewsTable of ContentsContentsPreface......................................................................................................................xvAcknowledgments.............................................................................................. xviiAcronyms and Abbreviations............................................................................ xix1 Introduction to Cancer....................................................................................11.1 Cancer......................................................................................................11.2 Epidemiology of Cancer........................................................................11.2.1 Cancer Trends............................................................................21.3 Prognostic Factors Associated with Cancer Outcomes ...................51.4 Economic Burden of Cancer.................................................................61.4.1 Health Expenditure..................................................................61.4.2 Healthcare Expenditure on Drugs.........................................71.5 Treatments for Cancer......................................................................... 101.6 Important Economic Concepts for Cost-Effectiveness ofCancer Interventions......................................................... 121.6.1 Economics, Health Economics, Economic Evaluation,and Pharmacoeconomics .................................................. 121.6.1.1 Value ......................................................................... 131.6.1.2 Allocative Efficiency............................................... 141.6.1.3 Technical Efficiency................................................ 151.6.1.4 Opportunity Cost.................................................... 161.6.1.5 Discounting.............................................................. 171.6.1.6 The Incremental Cost-Effectiveness Ratio .......... 181.6.1.7 The Cost-Effectiveness Plane................................. 191.6.1.8 Quality-Adjusted Life-Years (QALY) ...................221.7 Health Economic Evaluation and Cancer DrugDevelopment in Practice .........................................................................231.7.1 The Modern Paradigm................................................................... 241.8 Efficacy versus Effectiveness .............................................................261.9 Real-World Data ..................................................................................271.10 Economic versus Clinical Hypotheses .............................................291.11 Summary............................................................................................... 321.12 Exercises for Chapter 1........................................................................332 Important Outcomes for Economic Evaluation in Cancer Studies......352.1 Introduction .........................................................................................352.2 Important Common, Surrogate, and Novel Cancer Endpoints............... 362.2.1 Overall Survival......................................................................362.2.1.1 OS and Economic Evaluation ............................... 412.2.2 Surrogate Endpoints...............................................................462.3 HTAs with Surrogate Endpoints.......................................................532.4 Emerging Tumor-Centered Endpoints.............................................552.5 Demonstrating Value from Other Cancer Endpoints..................... 572.6 Summary...............................................................................................582.7 Exercises for Chapter 2........................................................................583 Health-Related Quality of Life for Cost-Effectiveness.......................... 593.1 Health-Related Quality of Life (HRQoL) in Cancer Patients......... 593.1.1 Limitations of Anti-Cancer Treatments...............................593.1.2 Why Collect HRQoL Data?....................................................603.1.3 Challenges with HRQoL in Cancer Studies........................ 613.2 Measuring Health-Related Quality of Life Outcomes forCommon Cancer Types .................................................................623.2.1 Condition-Specific Measures of HRQoL ............................ 623.2.2 Common General Condition-Specific Measures ofHRQoL in Cancer......................................................................................633.3 Measuring HRQoL for Economic Evaluation ................................. 673.3.1 EuroQol EQ-5D-3L and 5L....................................................683.3.2 EuroQol EQ-5D-5L................................................................. 693.4 Constructing Utilities.......................................................................... 703.5 Quality-Adjusted Life-Years (QALYs)................................................723.5.1 QALY Calculation in Cancer Trials......................................733.6 Economic Evaluation in the Absence of Utility Data:Mapping and Utility Studies ................................................................. 743.7 Sensitivity and Responsiveness of EQ-5D versus QLQ-C30HRQoL for Detecting Improvement in Cancer Patients ...................... 763.8 Measuring Post-Progression (PP) Utility: Some Approaches .......77Why Is Estimation of Utility between Disease Progressionand Death Relevant?............................................................... 78The Behavior of Utility in Cancer Patients betweenProgression and Death?.........................................................793.8.1 Plausible Post-Progression Utility Behavior ......................803.8.2 Non-Linear Models.................................................................823.9 HRQoL issues in Health Technology Appraisals of CancerDrugs .........................................................................................................873.10 Summary...............................................................................................893.11 Exercises for Chapter 3........................................................................894 Introductory Statistical Methods for Economic Evaluation inCancer............................................................................................................... 914.1 Introduction.......................................................................................... 914.2 Uncertainty and Variability................................................................ 914.2.1 Uncertainty..............................................................................924.2.2 Variability.................................................................................924.2.2.1 Hypothesis Testing.................................................934.3 Distributions: Cost, Utility, and Survival Data ...............................934.4 Important Measures Used in Cancer Trials.....................................954.4.1 Time-to-Event Endpoints.......................................................954.4.2 Median Survival.....................................................................964.4.3 Hazard Rate and Hazard Ratio............................................984.4.4 Hazard Ratio............................................................................994.4.5 Survival Rates and Proportions.......................................... 1014.4.6 Relationship between Hazard Rate and Survival Rate......1024.4.7 Transition Probability and Matrix...................................... 1034.4.8 Relation between Transition Probability andSurvival Rates .............................................................................. 1044.4.9 Proportional Hazards.......................................................... 1064.4.10 Mean Survival and Restricted Mean ................................ 1064.5 Simulation: Bootstrapping and Monte-Carlo Simulation............ 1094.5.1 Simulating Using Monte-Carlo Sampling......................... 1114.6 Analyzing Data from Cancer Trials................................................ 1114.6.1 Semi-Parametric Methods: The Cox PH Model............... 1114.6.1.1 Adjusting for Covariates with the Cox Model......1124.6.1.2 Using Hazard Ratios to Predict Survival Rates.....1134.6.2 Parametric Methods: Modeling Survival Data forExtrapolation ........................................................................... 1144.6.3 Advanced Modeling Techniques for Survival Data ....... 1184.6.3.1 Flexible Parametric Survival Models................. 1184.6.3.2 Applications in Cancer Surveillance ................. 1194.7 Issues in Fitting Models....................................................................1224.8 Handling Crossover, Treatment Switching, andSubsequent Anti-Cancer Therapy ............................................. 1234.8.1 Introduction to Treatment Switching................................. 1234.8.2 Types of Switching ............................................................... 1244.8.3 Implications of Switching.................................................... 1244.8.4 Handling Switching in Statistical Analyses .................... 1264.8.4.1 Intent-to-Treat (ITT)............................................... 1274.8.4.2 Per Protocol Analysis............................................ 1284.8.4.3 IPCW....................................................................... 1284.8.4.4 RPFSTM.................................................................. 1294.8.4.5 Two-Stage Adjustment Model ............................ 1314.8.4.6 Other Approaches: StructuralNested Mean Models (SNNM) ............................................................. 1314.9 Data Synthesis and Network Meta-Analyses................................ 1324.9.1 Mixed Treatment Comparisons ......................................... 1324.9.1.1 Direct Comparison................................................ 1334.9.1.2 Indirect Treatment Comparison (ITC) .............. 1334.9.1.3 Meta-Analysis ....................................................... 1344.9.1.4 Network of Evidence ........................................... 1344.9.2 Assumptions for Carrying Out MTCs .............................. 1344.10 Summary............................................................................................. 1384.11 Exercises for Chapter 4...................................................................... 1405 Collecting and Analysis of Costs from Cancer Studies...................... 1415.1 Types of Costs Typical of Cancer Trials.......................................... 1415.1.1 Categorization of Health Resource Use............................. 1425.1.2 Resource Use Monitoring.................................................... 1425.1.3 Baseline Characteristics and Health Resource Use......... 1435.1.4 Costs Determined by a Study Protocol.............................. 1445.2 Perspective of Analysis and Costs Collection................................ 1455.3 Collecting Health Resource Use across the TreatmentPathway ....................................................................................... 1465.3.1 Time Horizon ....................................................................... 1485.4 Costing Methods: Micro versus Macro Approach........................ 1505.4.1 Average versus Marginal and Incremental Cost.............. 1515.4.2 Inflation.................................................................................. 1525.4.3 Time Preference and Discounting...................................... 1535.5 Charges................................................................................................ 1545.5.1 Cost-to-Charge Ratios.......................................................... 1555.5.2 Other Non-Medical Costs (e.g. Societal Costs)................. 1555.6 Distribution of Costs.......................................................................... 1555.6.1 Transforming Cost Data....................................................... 1575.7 Handling Censored and Missing Costs ........................................ 1585.7.1 Strategies for Avoiding Missing Resource Data .............. 1605.7.2 Strategies for Analyzing Cost DataWhen Data Are Missing or Censored ..................................... 1605.7.3 Imputation Methods............................................................. 1615.8 Handling Future Costs...................................................................... 1625.9 Case Report Forms and Health Resource Use............................... 1645.10 Statistical Analyses of Costs ............................................................ 1655.11 Summary............................................................................................. 1725.12 Exercises for Chapter 5...................................................................... 1736 Designing Cost-Effectiveness into Cancer Trials................................. 1756.1 Introduction and Reasons for Collecting EconomicData in a Clinical Trial ........................................................................... 1756.2 Clinical Trial Designs for Cancer Studies...................................... 1786.2.1 Clinical Trial Designs........................................................... 1786.2.2 Interim Analyses and Data Monitoring Committees(DMC)..................................................................................... 1886.3 Planning a Health Economic Evaluation in a Clinical Trial ....... 1916.3.1 Important Considerations When Designinga Cancer Study for Economic Evaluation ............................................... 1916.3.2 Integrating Economic Evaluation in a Clinical Trial:Considerations .......................................................................... 1946.3.3 Endpoints and Outcomes.................................................... 1966.3.3.1 Timing of Measurements..................................... 1986.3.3.2 Trial Design............................................................ 1986.3.3.3 CRF Design............................................................ 1996.3.3.4 Sample Size Methods for Cost-Effectiveness....... 1996.3.3.5 Sample Size Formulae forCost-Effectiveness: Examples .................................................... 2016.3.4 Treatment Pathways.............................................................2046.3.5 Time of Generic/Competition Entry..................................2046.3.6 Treatment Compliance.........................................................2056.3.7 Identify Subgroups/Heterogeneity....................................2066.3.8 Early ICER/INMB.................................................................2066.3.9 Multicenter Trials.................................................................. 2076.4 Case Study of Economic Evaluation of Cancer Trials................... 2106.4.1 TA516 Cabozanitib + Vandetanib....................................... 2106.5 Summary............................................................................................. 2106.6 Exercises for Chapter 6...................................................................... 2137 Models for Economic Evaluation of Cancer........................................... 2157.1 Types of Health Economic Models.................................................. 2157.2 Decision Tree Models........................................................................ 2157.2.1 Further Possible Improvements to theDecision Model ......................................................................................2247.3 Markov Models..................................................................................2267.4 Continuous Time Markov Models...................................................2307.5 The Partitioned Survival Model...................................................... 2317.5.1 Developing an Economic Model Using Patient-LevelData Using a Partitioned Survival Model Approach ......................... 2317.5.1.1 Modeling the Efficacy Data (Survival Data)............................ 2317.5.2 Case Study of an Economic Model Using Patient-Level Data: A Partitioned Survival Model..................................................... 2327.5.3 Crossover...............................................................................2367.6 Summary of Cost-Effectiveness Models for CancerUsed in HTA Submissions ....................................................................2397.7 Summary............................................................................................. 2437.8 Exercises for Chapter 7...................................................................... 2438 Real-World Data in Cost-EffectivenessStudies on Cancer ....................................................................................... 2498.1 Introduction to Real-World Data..................................................... 2498.2 Using RWD to Support Cost-Effectiveness Analysis ................... 2518.3 Strengths and Limitations of Using RWD to SupportCost-Effectiveness Analysis .....................................................2538.3.1 Limitations.............................................................................2558.3.2 Internal Validity versus Generalizability..........................2568.4 Sources for RWD Generation........................................................... 2578.4.1 Registries ...............................................................................2608.4.2 Audits .................................................................................... 2618.4.3 Primary Care Databases: CPRD, THIN, QResearch........ 2628.4.4 Insurance Claims Databases...............................................2638.4.5 Digital Data Sources, Social Media and Applications....... 2638.4.6 Commercial Data Sources...................................................2648.4.7 Pragmatic Clinical Trials.....................................................2648.4.8 Prospective Observational Research Studies...................2658.4.9 Case Control Studies............................................................2658.5 Using Cancer Registries....................................................................2658.5.1 Examples of Registries in the UK for RWE ...................... 2678.6 Statistical Analyses of RWD: Addressing Selection Bias.............2688.6.1 Propensity Score Modeling.................................................2688.6.2 Instrumental Variable Methods.......................................... 274Results..................................................................................................2778.7 Summary and Conclusion................................................................ 2798.8 Exercises for Chapter 8...................................................................... 2819 Reporting and Interpreting Results of Cost-EffectivenessAnalyses from Cancer Trials.....................................................................2839.1 Interpreting Incremental Costs and QALYs...................................2839.1.1 Informative Censoring.........................................................2849.2 Interpreting Incremental QALYs..................................................... 2879.3 Relationship between Costs and QALYs........................................2909.4 Interpreting the ICER and the Cost-Effectiveness Plane.............. 2929.4.1 Uncertainty............................................................................ 2929.5 Presenting and Interpreting Results from UncertaintyAnalysis ....................................................................................................2969.6 Bayesian Sensitivity Analysis...........................................................3069.6.1 Limitations of the ICER and Using the INMB..................3079.7 Presenting and Interpreting Results from Value ofInformation Analyses .............................................................................3089.8 Challenges of VOI Analysis in Healthcare Decisions................... 3169.9 Summary ............................................................................................ 3179.10 Exercises for Chapter 9...................................................................... 317Technical Appendix for Chapter 9.............................................................. 318A9.1 Simulation.............................................................................. 318A9.2 Bayesian PSA......................................................................... 319A9.3 Value of Information............................................................ 320Before Any Data Is Observed........................................................... 321After Data Have Been Observed...................................................... 32110 Factors Predictive of HTA Success and the Global Landscape.......... 32310.1 Introduction........................................................................................ 32310.2 Cancer Drugs Rejected by NICE...................................................... 32310.3 Summary of Criticisms of Economic Models of Cancer.............. 32410.4 Factors Predictive of Successful HTAs in Cancer..........................33510.5 The Changing Pace of the Reimbursement Environment .......... 34110.6 Reimbursement and Payer Evidence RequirementsAcross Different Countries ...........................................................34410.6.1 Canada....................................................................................34510.6.2 France......................................................................................34510.6.3 Germany.................................................................................34610.6.4 Italy.........................................................................................34710.6.5 Spain.......................................................................................34710.6.6 Australia.................................................................................34810.6.7 United Kingdom...................................................................34910.7 Pricing and Reimbursement Environment in theUnited States.......................................................................................34910.8 Value-Based Pricing (VBP) for Cancer Drugs................................35010.9 Risk-Sharing Scheme ........................................................................ 35210.10 The Future of Cost-Effectiveness of Cancer Treatments..............35610.10.1 Future Research: Methodology..........................................35610.10.2 Future Reimbursement Landscape....................................358Budget Impact Threshold................................................... 35910.10.2.1 Automatic Funding for Highly SpecializedDrugs for Rare Diseases.....................................................35910.10.2.2 Fast-Track Appraisals......................................... 35910.11 Summary.............................................................................................36010.12 Exercises for Chapter 10....................................................................360References............................................................................................................ 361Additional Bibliography.............................................................................. 394Chapter 1............................................................................................. 394Chapter 3............................................................................................. 395Chapter 4............................................................................................. 395Chapter 5............................................................................................. 399Chapter 7............................................................................................. 399Chapter 9............................................................................................. 399Index...................................................................................................................... 401

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Book SynopsisPraise for the first edition:Given the author's years of experience as a statistician and as a founder of the first DMC in pharmaceutical industry trials, I highly recommend this booknot only for experts because of its cogent and organized presentation, but more importantly for young investigators who are seeking information about the logistical and philosophical aspects of a DMC. -S. T. Ounpraseuth, The American Statistician In the first edition of this well-regarded book, the author provided a groundbreaking and definitive guide to best practices in pharmaceutical industry data monitoring committees (DMCs). Maintaining all the material from the first edition and adding substantial new material, Data and Safety Monitoring Committees in Clinical Trials, Second Edition is ideal for training professionals to serve on their first DMC as well as for experienced clinical and biostatistical DMC members, sponsor and regTrade Review "The book by Dr. Herson is written amazingly well. The book concentrates on pharmaceutical industrysponsored confirmatory clinical trials and can serve as excellent sources of knowledge for all the aspects of data safety monitoring committee (DMC) activities. A great feature of the book is the “DMCounselor” section at the end of each chapter, covering nearly 70 questions with answers, of which about 33% are for a DMC Chair, 30% for the DMC Biostatistician member, 30% for a DMC physician member, and the rest are for others (e.g., project manager)."~ Daniel Jia, Journal of Biopharmaceutical Statistics"Nicely written and readable cover-to-cover, the author walks through every facet of a Data Monitoring Committee (DMC) beginning with an overview of their past and current place in drug development, to how they are organized and interfaced with other committees, and on to the specifics of how a typical meeting is split into an open and closed session. From there it moves on to clinical issues, including how SAEs are categorized, statistical methods, including Bayesian and frequentist conditional power calculations, common biases and pitfalls, and guidance for how DMC decisions are made. It concludes with emerging issues due to new clinical trial designs mandated by the FDA to speed up the drug development process."~Donna Pauler Ankerst, BiometricsTable of ContentsPreface to the Second Edition. Introduction. Organization of a Safety Monitoring Program for a Confirmatory Trial. Meetings. Clinical Issues. Statistical Issues. Biases and Pitfalls. DMC Decisions. Emerging issues. Appendix.

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Book SynopsisClinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial.After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.Trade Review… There is much good material in this book. The individual chapters are well written and cover the technical aspects as well. A major strength is the ordering of topics to follow the thought process used in the development and implementation of a protocol from defining the question to reporting results. There are careful discussions on fundamental principles and the pivotal role played by statistics is well brought out. … there is much that practicing pharmaceutical statisticians will find useful in this book. They will find the coverage of fundamental principles useful and the technical content of the book a good reference source. …—Pharmaceutical Statistics, 2010… fits the need for a contemporary text and handbook that is oriented toward the clinical trial statistician. I highly recommend it and look forward to using it as both a primary and supplemental text in our curriculum, as well as a research resource.—James J. Dignam, University of Chicago, JASA, March 2009The (technical) statistical content is the main focus of the book and this is what helps it to stand apart from most others on clinical trials (even the more obviously statistically orientated ones). It takes the reader to quite a technical background that would serve him or her well if moving on to research problems in the various areas covered, yet does not lose sight of practical issues. … For those of us with the interest (and need) to grapple with these more statistical issues, I wholeheartedly recommend it.—Biometrics, December 2008…The book is very well written and clear. … the authors generally strike the right balance for the intended audience. The inclusion of many historically important as well as contemporary examples to illustrate various points throughout the text is a major strength, as is the inclusion of several modern topics not seen in other texts. As a basis for a course in clinical trials for graduate students in biostatistics, this book is outstanding. In addition, statisticians in the pharmaceutical industry, government, or academia … will find this text extremely informative and useful.” —Michael P. McDermott, University of Rochester Medical Center, Journal of Biopharmaceutical Statistics, 2008Table of ContentsPreface. Introduction to Clinical Trials. Defining the Question. Study Design. Sample Size. Randomization. Data Collection and Quality Control. Survival Analysis. Longitudinal Data. Quality of Life. Data Monitoring and Interim Analysis. Selected Issues in the Analysis. Closeout and Reporting. Special Topics. Appendix. References. Index.

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Book Synopsis

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Book SynopsisThis book provides a comprehensive introduction to statistical methods for designing early phase dose-finding clinical trials. It will serve as a textbook or handbook for graduate students and practitioners in biostatistics and clinical investigators who are involved in designing, conducting, monitoring, and analyzing dose-finding trials. The book will also provide an overview of advanced topics and discussions in this field for the benefit of researchers in biostatistics and statistical science. Beginning with backgrounds and fundamental notions on dose finding in early phase clinical trials, the book then provides traditional and recent dose-finding designs of phase I trials for, e.g., cytotoxic agents in oncology, to evaluate toxicity outcome. Included are rule-based and model-based designs, such as 3 + 3 designs, accelerated titration designs, toxicity probability interval designs, continual reassessment method and related designs, and escalation overdose control designs. This book also covers more complex and updated dose-finding designs of phase I-II and I/II trials for cytotoxic agents, and cytostatic agents, focusing on both toxicity and efficacy outcomes, such as designs with covariates and drug combinations, maximum tolerated dose-schedule finding designs, and so on.Trade Review“The book under review is intended to serve as a brief handbook for graduate students and biostatistics, as well as for oncologists who are involved in the analysis of methods for developing optimal doses for treating cancer in the early stages.” (Fatima T. Adylova, zbMATH 1427.92001, 2020)Table of Contents1. Introduction.- 2. Dose Finding in Early Phase Clinical Trials.- 3. Rule.- Based Designs.- 4. Continual Reassessment Method Designs.- 5. Escalation with Overdose Control Designs.- 6. Decision.- Theoretic Designs.- 7. Complex Designs.

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