1st Edition

Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

    376 Pages
    by Chapman & Hall

    376 Pages
    by Chapman & Hall

    "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.

    1. Overview of Drug Development
    2. Introduction

      Drug Discovery

      Target Identi_cation and Validation

      Irrational Approach

      Rational Approach



      Preclinical Development

      Objectives of Preclinical Development




      Intraspecies and Interspecies Scaling

      Clinical Development

      Overview of Clinical Development

      Classical Clinical Trial Paradigm

      Adaptive Trial Design Paradigm

      New Drug Application


    3. Clinical Development Plan and Clinical Trial Design
    4. 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


    5. Clinical Development Optimization
    6. 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


    7. Globally Optimal Adaptive Trial Designs
    8. 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


      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

    9. Trial Design for Precision Medicine
    10. 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


    11. Clinical Trial with Survival Endpoint
    12. 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


      Progressive Disease Model

      Piecewise Model for Delayed Drug Effect


      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


    13. Practical Multiple Testing Methods in Clinical Trials
    14. 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


    15. Missing Data Handling in Clinical Trials
    16. 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

    17. Special Issues and Resolutions
    18. 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


      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


    19. Issues and Concepts of Data Monitoring Committees
    20. 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

    21. Controversies in Statistical Science

    What is a Science?

    Similarity Principle

    Simpson's Paradox


    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


    Mark Chang, John Balser, Jim Roach, Robin Bliss

    "This is the first edition of a comprehensive book covering the most recent methodology on innovative clinical trial designs for drugs and biological products. It is a great reference book for statisticians, clinicians, and other stakeholders involved in drug discovery and development. ... Chang et al aimed to provide the statistical framework to reach the overall development program optimizations in this book. In addition, innovative methodology to mitigate the risks of failed efficacy, safety, strategy, commercial and operation failures have been described by Chang et al. Special techniques such as clinical trial simulations are highly recommended by the authors. ....
    In summary, this is an excellent reference book for statisticians, clinicians, and all stakeholders involved in clinical development program with a common goal to reach clinical development optimization.”
    —Holly Huang in the Journal of Biopharmaceutical Statistics,  October 2019

    "The book has a number of detailed examples, and SAS and R code to implement some of the methods described in the text...In summary, this book covers a wide range of interesting topics in clinical trials, and provides an appealing and useful reference to researchers."
    - Ionut Bebu, JASA 2020

    "This first edition of this book provides the most recent methodology and statistical considerations in the design and management of clinical trials. It is focused on professionals in drug development, specifically statisticians and clinical researchers...It is, however, a useful insight into strategies for specific situations including personalized medicine, missing data, adaptive design, and multiple testing. The authors include a large number of practical clinical trials from various therapeutic areas, and they put emphasis on the use of clinical trial simulations...The authors present a remarkable amount of SAS code examples that could be directly used in daily practice. An extensive overview of modern innovative strategies for clinical trials helps to broaden the horizons of scientists interested in drug or treatment development."
    - Iveta Selingerová, ISCB News, July 2020