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 UniversityThe 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 andRegulatory 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 ControlBasket 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 CovariateMultistage 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 EffectOncology 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 TestsAdaptive 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