Statistical Methods for Drug Safety PDF
Preview Statistical Methods for Drug Safety
Statistical Methods for Drug Safety Robert D. Gibbons University of Chicago Illinois, USA Anup K. Amatya New Mexico State University Las Cruces, USA © 2016 by Taylor & Francis Group, LLC Editor-in-Chief Shein-Chung Chow, Ph.D., Professor, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina Series Editors Byron Jones, Biometrical Fellow, Statistical Methodology, Integrated Information Sciences, Novartis Pharma AG, Basel, Switzerland Jen-pei Liu, Professor, Division of Biometry, Department of Agronomy, National Taiwan University, Taipei, Taiwan Karl E. Peace, Georgia Cancer Coalition, Distinguished Cancer Scholar, Senior Research Scientist and Professor of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia Bruce W. Turnbull, Professor, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York Published Titles Adaptive Design Methods in Bayesian Methods in Epidemiology Clinical Trials, Second Edition Lyle D. Broemeling Shein-Chung Chow and Mark Chang Bayesian Methods in Health Economics Adaptive Designs for Sequential Gianluca Baio Treatment Allocation Bayesian Missing Data Problems: EM, Alessandro Baldi Antognini and Data Augmentation and Noniterative Alessandra Giovagnoli Computation Adaptive Design Theory and Ming T. Tan, Guo-Liang Tian, Implementation Using SAS and R, and Kai Wang Ng Second Edition Bayesian Modeling in Bioinformatics Mark Chang Dipak K. Dey, Samiran Ghosh, Advanced Bayesian Methods for Medical and Bani K. Mallick Test Accuracy Benefit-Risk Assessment in Lyle D. Broemeling Pharmaceutical Research and Advances in Clinical Trial Biostatistics Development Nancy L. Geller Andreas Sashegyi, James Felli, and Rebecca Noel Applied Meta-Analysis with R Ding-Geng (Din) Chen and Karl E. Peace Biosimilars: Design and Analysis of Follow-on Biologics Basic Statistics and Pharmaceutical Shein-Chung Chow Statistical Applications, Second Edition James E. De Muth Biostatistics: A Computing Approach Stewart J. Anderson Bayesian Adaptive Methods for Clinical Trials Causal Analysis in Biomedicine and Scott M. Berry, Bradley P. Carlin, Epidemiology: Based on Minimal J. Jack Lee, and Peter Muller Sufficient Causation Mikel Aickin Bayesian Analysis Made Simple: An Excel GUI for WinBUGS Clinical and Statistical Considerations Phil Woodward in Personalized Medicine Claudio Carini, Sandeep Menon, Bayesian Methods for Measures of and Mark Chang Agreement Lyle D. Broemeling © 2016 by Taylor & Francis Group, LLC Clinical Trial Data Analysis using R DNA Microarrays and Related Genomics Ding-Geng (Din) Chen and Karl E. Peace Techniques: Design, Analysis, and Interpretation of Experiments Clinical Trial Methodology David B. Allison, Grier P. Page, Karl E. Peace and Ding-Geng (Din) Chen T. Mark Beasley, and Jode W. Edwards Computational Methods in Biomedical Dose Finding by the Continual Research Reassessment Method Ravindra Khattree and Dayanand N. Naik Ying Kuen Cheung Computational Pharmacokinetics Elementary Bayesian Biostatistics Anders Källén Lemuel A. Moyé Confidence Intervals for Proportions and Empirical Likelihood Method in Survival Related Measures of Effect Size Analysis Robert G. Newcombe Mai Zhou Controversial Statistical Issues in Frailty Models in Survival Analysis Clinical Trials Andreas Wienke Shein-Chung Chow Generalized Linear Models: A Bayesian Data and Safety Monitoring Committees Perspective in Clinical Trials Dipak K. Dey, Sujit K. Ghosh, Jay Herson and Bani K. Mallick Design and Analysis of Animal Studies in Handbook of Regression and Modeling: Pharmaceutical Development Applications for the Clinical and Shein-Chung Chow and Jen-pei Liu Pharmaceutical Industries Design and Analysis of Bioavailability and Daryl S. Paulson Bioequivalence Studies, Third Edition Inference Principles for Biostatisticians Shein-Chung Chow and Jen-pei Liu Ian C. Marschner Design and Analysis of Bridging Studies Interval-Censored Time-to-Event Data: Jen-pei Liu, Shein-Chung Chow, Methods and Applications and Chin-Fu Hsiao Ding-Geng (Din) Chen, Jianguo Sun, Design and Analysis of Clinical Trials for and Karl E. Peace Predictive Medicine Introductory Adaptive Trial Designs: Shigeyuki Matsui, Marc Buyse, A Practical Guide with R and Richard Simon Mark Chang Design and Analysis of Clinical Trials with Time-to-Event Endpoints Joint Models for Longitudinal and Time- to-Event Data: With Applications in R Karl E. Peace Dimitris Rizopoulos Design and Analysis of Non-Inferiority Measures of Interobserver Agreement Trials and Reliability, Second Edition Mark D. Rothmann, Brian L. Wiens, Mohamed M. Shoukri and Ivan S. F. Chan Medical Biostatistics, Third Edition Difference Equations with Public Health A. Indrayan Applications Lemuel A. Moyé and Asha Seth Kapadia Meta-Analysis in Medicine and Health Policy DNA Methylation Microarrays: Dalene Stangl and Donald A. Berry Experimental Design and Statistical Analysis Sun-Chong Wang and Arturas Petronis © 2016 by Taylor & Francis Group, LLC Mixed Effects Models for the Population Sample Size Calculations in Clinical Approach: Models, Tasks, Methods and Research, Second Edition Tools Shein-Chung Chow, Jun Shao Marc Lavielle and Hansheng Wang Modeling to Inform Infectious Disease Statistical Analysis of Human Growth Control and Development Niels G. Becker Yin Bun Cheung Monte Carlo Simulation for the Statistical Design and Analysis of Pharmaceutical Industry: Concepts, Stability Studies Algorithms, and Case Studies Shein-Chung Chow Mark Chang Statistical Evaluation of Diagnostic Multiple Testing Problems in Performance: Topics in ROC Analysis Pharmaceutical Statistics Kelly H. Zou, Aiyi Liu, Andriy Bandos, Alex Dmitrienko, Ajit C. Tamhane, Lucila Ohno-Machado, and Howard Rockette and Frank Bretz Statistical Methods for Clinical Trials Noninferiority Testing in Clinical Trials: Mark X. Norleans Issues and Challenges Statistical Methods for Drug Safety Tie-Hua Ng Robert D. Gibbons and Anup K. Amatya Optimal Design for Nonlinear Response Statistical Methods in Drug Combination Models Studies Valerii V. Fedorov and Sergei L. Leonov Wei Zhao and Harry Yang Patient-Reported Outcomes: Statistics in Drug Research: Measurement, Implementation and Methodologies and Recent Interpretation Developments Joseph C. Cappelleri, Kelly H. Zou, Shein-Chung Chow and Jun Shao Andrew G. Bushmakin, Jose Ma. J. Alvir, Statistics in the Pharmaceutical Industry, Demissie Alemayehu, and Tara Symonds Third Edition Quantitative Evaluation of Safety in Drug Ralph Buncher and Jia-Yeong Tsay Development: Design, Analysis and Survival Analysis in Medicine and Reporting Genetics Qi Jiang and H. Amy Xia Jialiang Li and Shuangge Ma Randomized Clinical Trials of Theory of Drug Development Nonpharmacological Treatments Isabelle Boutron, Philippe Ravaud, and Eric B. Holmgren David Moher Translational Medicine: Strategies and Statistical Methods Randomized Phase II Cancer Clinical Dennis Cosmatos and Shein-Chung Chow Trials Sin-Ho Jung Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research Chul Ahn, Moonseong Heo, and Song Zhang © 2016 by Taylor & Francis Group, LLC Statistical Methods for Drug Safety Robert D. Gibbons University of Chicago Illinois, USA Anup K. Amatya New Mexico State University Las Cruces, USA © 2016 by Taylor & Francis Group, LLC CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2016 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20150604 International Standard Book Number-13: 978-1-4665-6185-4 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. 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To my family and friends A.K.A. © 2016 by Taylor & Francis Group, LLC © 2016 by Taylor & Francis Group, LLC Contents Preface xv Acknowledgments xix 1 Introduction 1 1.1 Randomized Clinical Trials . . . . . . . . . . . . . . . . . . . 2 1.2 Observational Studies . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The Problem of Multiple Comparisons . . . . . . . . . . . . . 5 1.4 The Evolution of Available Data Streams . . . . . . . . . . . 6 1.5 The Hierarchy of Scientific Evidence . . . . . . . . . . . . . . 7 1.6 Statistical Significance . . . . . . . . . . . . . . . . . . . . . . 8 1.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Basic Statistical Concepts 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Relative Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Odds Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Statistical Power . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . 17 2.5.1 Example with a Closed Form Solution . . . . . . . . . 19 2.5.2 Example without a Closed Form Solution . . . . . . . 20 2.5.3 Bayesian Statistics . . . . . . . . . . . . . . . . . . . . 21 2.5.4 Example . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Non-linear Regression Models . . . . . . . . . . . . . . . . . 23 2.7 Causal Inference . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.7.1 Counterfactuals . . . . . . . . . . . . . . . . . . . . . . 25 2.7.2 Average Treatment Effect . . . . . . . . . . . . . . . . 25 3 Multi-level Models 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Issues Inherent in Longitudinal Data . . . . . . . . . . . . . 29 3.2.1 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.2 Missing Data . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.3 Irregularly Spaced Measurement Occasions . . . . . . 30 ix © 2016 by Taylor & Francis Group, LLC x Contents 3.3 Historical Background . . . . . . . . . . . . . . . . . . . . . . 31 3.4 StatisticalModelsfortheAnalysisofLongitudinaland/orClus- tered Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.1 Mixed-effects RegressionModels . . . . . . . . . . . . 32 3.4.1.1 Random Intercept Model . . . . . . . . . . . 34 3.4.1.2 Random Intercept and Trend Model . . . . . 36 3.4.2 Matrix Formulation . . . . . . . . . . . . . . . . . . . 37 3.4.3 Generalized Estimating Equation Models . . . . . . . 39 3.4.4 Models for CategoricalOutcomes . . . . . . . . . . . . 40 4 Causal Inference 43 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Propensity Score Matching . . . . . . . . . . . . . . . . . . . 44 4.2.1 Illustration . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3 Marginal Structural Models . . . . . . . . . . . . . . . . . . . 50 4.3.1 Illustration . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4 Instrumental Variables . . . . . . . . . . . . . . . . . . . . . 55 4.4.1 Illustration . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5 Differential Effects . . . . . . . . . . . . . . . . . . . . . . . . 61 5 Analysis of Spontaneous Reports 69 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.2 Proportional Reporting Ratio . . . . . . . . . . . . . . . . . 70 5.2.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.3 Bayesian Confidence PropagationNeural Network (BCPNN) 72 5.4 Empirical Bayes Screening . . . . . . . . . . . . . . . . . . . 77 5.5 Multi-item Gamma Poisson Shrinker . . . . . . . . . . . . . 80 5.6 Bayesian Lasso Logistic Regression . . . . . . . . . . . . . . 83 5.7 Random-effect PoissonRegression . . . . . . . . . . . . . . . 87 5.7.1 Rate Multiplier . . . . . . . . . . . . . . . . . . . . . . 88 5.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6 Meta-analysis 93 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.2 Fixed-effect Meta-analysis . . . . . . . . . . . . . . . . . . . 94 6.2.1 Correlation Coefficient . . . . . . . . . . . . . . . . . . 94 6.2.2 Mean Difference . . . . . . . . . . . . . . . . . . . . . 95 6.2.3 Relative Risk . . . . . . . . . . . . . . . . . . . . . . . 95 6.2.3.1 Inverse Variance Method . . . . . . . . . . . 97 6.2.3.2 Mantel-Haenszel Method . . . . . . . . . . . 97 © 2016 by Taylor & Francis Group, LLC
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