Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials  book cover
1st Edition

Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

ISBN 9780815379447
Published March 20, 2019 by Chapman and Hall/CRC
376 Pages

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Book Description

"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.

Table of Contents

  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

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"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