Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development…. Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.

Overview of Drug Development Introduction Drug Discovery Target Identi_cation and Validation Irrational Approach Rational Approach Biologics NanoMedicine Preclinical Development Objectives of Preclinical Development Pharmacokinetics Pharmacodynamics Toxicology Intraspecies and Interspecies Scaling Clinical Development Overview of Clinical Development Classical Clinical Trial Paradigm Adaptive Trial Design Paradigm New Drug Application Summary Clinical Development Plan and Clinical Trial Design Clinical Development Program Unmet Medical Needs & Competitive Landscape Therapeutic Areas Value proposition Prescription Drug Global Pricing Clinical Development Plan Clinical Trials Placebo, Blinding and Randomization Trial Design Type Confounding Factors Variability and Bias Randomization Procedure Clinical Trial Protocol Target Population Endpoint Selection Proof of Concept Trial Sample Size and Power Bayesian Power for Classical Design Summary Clinical Development Optimization Benchmarks in Clinical Development Net Present Value and Risk-Adjusted NPV Method Clinical Program Success Rates Failure Rates by Reason Costs of Clinical Trials Time-to-Next Phase, Clinical Trial Length and Regulatory Review Time Rates of Competitor Emerging Optimization of Clinical Development Program Local Versus Global Optimizations Stochastic Decision Process for Drug Development Time Dependent Gain g, Determination of Transition Probabilities Example of CDP Optimization Updating Model Parameters Clinical Development Program with Adaptive Design Summary Globally Optimal Adaptive Trial Designs Common Adaptive Designs Group Sequential Design Test Statistics Commonly Used Stopping Boundaries Sample Size Reestimation Design Test Statistic Rules of Stopping and Sample-Size Adjustment Simulation Examples Pick-Winner-Design Shun-Lan-Soo Method for Three-Arm Design K-Arm Pick-Winner Design Global Optimization of Adaptive Design - Case Study Medical Needs for COPD COPD Market Indacaterol Trials US COPD Phase II Trial Results Optimal Design Summary & Discussions Trial Design for Precision Medicine Introduction Overview of Classical Designs with Biomarkers Biomarker-enrichment Design Biomarker-Stratified Design Sequential Testing Strategy Design Marker-based Strategy Design Hybrid Design Overview of Biomarker-Adaptive Designs Adaptive Accrual Design Biomarker-Informed Group Sequential Design Biomarker-Adaptive Threshold Design Adaptive Signature Design Cross-Validated Adaptive Signature Design Trial Design Method with Biomarkers Impact of Assay Sensitivity and Specificity Biomarker-Stratified Design Biomarker-Adaptive Winner Design Biomarker-Informed Group Sequential Design Basket and Population-Adaptive Designs Basket Design Method with Familywise Error Control Basket Design for Cancer Trial with Imatinib Methods based on Similarity Principle Summary Clinical Trial with Survival Endpoint Overview of Survival Analysis Basic Taxonomy Nonparametric Approach Proportional Hazard Model Accelerated Failure Time Model Frailty Model Maximum Likelihood Method Landmark Approach and Time-Dependent Covariate Multistage Models for Progressive Disease Introduction Progressive Disease Model Piecewise Model for Delayed Drug Effect Introduction Piecewise Exponential Distribution Mean and Median Survival Times Weighted LogRank Test for Delayed Treatment Effect Oncology Trial with Treatment Switching Descriptions of the Switching Problem Treatment Switching Inverse Probability of Censoring Weighted LogRank Test Removing Treatment Switch Issue by Design Competing Risks Competing Risks as Bivariate Random Variable Solution to Competing Risks Model Competing Progressive Disease Model Hypothesis Test Method Threshold Regression with First-Hitting-Time Model Multivariate Model with Biomarkers Summary Practical Multiple Testing Methods in Clinical Trials Multiple-Testing Problems Sources of Multiplicity Multiple-Testing Taxonomy Union-Intersection Testing Single-Step Procedure Stepwise Procedures Single-Step Progressive Parametric Procedure Power Comparison of Multiple Testing Methods Application to Armodafinil Trial Intersection-Union Testing The Need for Coprimary Endpoints Conventional Approach Average Error Method Li-Huque's Method Application to a Glaucoma Trial Priority Winner Test for Multiple Endpoints Finkelstein-Schoenfeld's Method Win-Ratio Test Application to Charm Trial Summary Missing Data Handling in Clinical Trials Missing Data Problems Missing Data Issue and Its Impact Missing Mechanism Implementation of Analysis Methods Trial Data Simulation Single Imputation Methods Methods without Specified Mechanics of Missing Inverse-Probability Weighting Method Multiple Imputation Method Tipping Point Analysis for MNAR Mixture of Paired and Unpaired Data Comparisons of Different Methods Regulatory and Operational Perspective Special Issues and Resolutions Overview Drop-Loser Design Based on Efficacy and Safety Multi-stage Design with Treatment Selection Dunnett Test with Drop-losers Drop-Loser Design with Gatekeeping Procedure Drop-loser Design with Adjustable Sample Size Drop-Loser Rules in Term of Efficacy and Safety Simulation Study Clinical Trial Interim Analysis with Survival Endpoint Hazard Ratio versus Number of Deaths Conditional Power Prediction of Timing for Target Number of Events Power and Sample Size for One-Arm Survival Trial Design Estimation of Treatment Effect with Interim Blinded Data Likelihood MLE Method Bayesian Posterior Analysis of Toxicology Study with Unexpected Deaths Fisher versus Barnard's Exact Test Methods Wald statistic Fisher's Conditional Exact Test p-value Barnard's Unconditional Exact Test p-value Power Comparisons of Fisher's versus Barnard's Tests Adaptive Design with Mixed Endpoints Summary Issues and Concepts of Data Monitoring Committees Overview of the DMC Operation of the DMC Role of the DMC Biostatistician Requirement for a DMC Use of a DMC in Rare Disease Studies Statistical methods for Safety Monitoring Statistical methods for interim efficacy analysis Summary and Discussion Controversies in Statistical Science What is a Science? Similarity Principle Simpson's Paradox Causality Type-I Error Rate and False Discovery Rate Multiplicity Challenges Regression with Time-Dependent Variables Hidden Confounders Controversies in Dynamic Treatment Regime Paradox of Understanding Summary and Recommendations

Dr. Mark Chang is Sr. Vice President, Strategic Statistical Consulting at Veristat. Before joining Veristat, Chang served various strategic roles in AMAG and Millennium Pharmaceuticals, including Vice President of Biometrics at AMAG and director and scientific fellow at Millennium/Takeda. Chang is a fellow of the American Statistical Association and an adjunct professor of Biostatistics at Boston University. He is a co-founder of the International Society for Biopharmaceutical Statistics, co-chair of the Biotechnology Industry Organization (BIO) Adaptive Design Working Group, and a member of the Multiregional Clinical Trial (MRCT) Expert Group. Hehas published 11 books, including Monte Carlo Simulation for the Pharmaceutical Industry, Adaptive Design Theory and Implementation Using SAS and R, Modern Issues and Methods in Biostatistics, Paradoxes in Scientific Inference, and Principles of Scientific Methods. John Balser, PhD, co-founder and President of Veristat, has developed the company as industry leaders in areas of clinical monitoring, data management, biostatistics and programming, medical writing, and project management. John is actively involved with clinical projects in his role as one of Veristat’s principal statistical consultants. In this role, he assists clients with clinical study design and program development based on his many years of experience in the statistical aspects of clinical research. He is often called upon to assist clients on a variety of statistical issues at meetings with regulatory agencies. Prior to founding Veristat in 1994, John served as Vice President, Biostatistics, and Data Management at Medical & Technical Research Associates, Inc. He has held positions of increasing responsibility in the biostatistics departments at various pharmaceutical companies including E.R. Squibb, Biogen, and Miles. John received his MS and PhD in Biometrics from Cornell University, and has been actively engaged in clinical biostatistics for over 25 years. John is an avid runner and has competed in the Boston Marathon. Robin Bliss, PhD joined Veristat in October, 2011 and has served as Director, Biostatistics since October, 2017. Through her experience at Veristat, Dr. Bliss has implemented complex adaptive designs across clinical trials in Phases I, II, and III as well as seamless Phase I/II and II/III trials. She has also provided strategic advice to sponsor companies, including representation of such companies at regulatory agencies, participation with scientific advisory committees, performance of simulation studies, and other consulting services. Dr. Bliss has taught conference short courses in adaptive design as well as statistical courses as a university adjunct faculty member. Prior to Veristat, Dr. Bliss held a post-doctoral fellowship position at Brigham and Women’s Hospital (Boston) in the Orthopedic and Arthritics Center for Outcomes Research. Dr. Bliss earned her PhD in Biostatistics from Boston University where her research focused on spatial and environmental statistics. James M. Roach, MD, FACP, FCCP joined Pulmatrix as their Chief Medical Officer (CMO) in November 2017. Dr. Roach served as the CMO at Veristat, Inc for the year prior to joining Pulmatrix, and prior to Veristat served as the Senior Vice President, Development and CMO at Momenta Pharmaceuticals, Inc. from 2008-2016. From 2002-2008 Dr. Roach was the Senior Vice President, Medical Affairs at Sepracor, Inc. Dr. Roach has also held senior clinical research and/or medical affairs positions at Millennium Pharmaceuticals, Inc., LeukoSite, Inc., Medical and Technical Research Associates, Inc. and Astra USA. Dr. Roach held an academic appointment at Harvard Medical School for close to 25 years and has been an Associate Physician at Brigham and Women’s Hospital (BWH) and member of the BWH Pulmonary and Critical Care Medicine Division since 1993. He received his B.A. in Biology and Philosophy from the College of the Holy Cross and his M.D. from Georgetown University School of Medicine. Dr. Roach completed his residency in Internal Medicine and fellowships in Pulmonary Disease and Critical Care Medicine at Walter Reed Army Medical Center in Washington, D.C., and served in the US Army Medical Corps for ten years. Dr. Roach is board certified in Internal Medicine and Pulmonary Disease, and is a Fellow of the American College of Physicians (ACP) and the American College of Chest Physicians (ACCP).