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

Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

1st Edition

By Mark Chang, John Balser, Jim Roach, Robin Bliss

Chapman and Hall/CRC

362 pages

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Hardback: 9780815379447
pub: 2019-03-29
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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

    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

  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

    Summary

  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

    Summary

  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

    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

  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

    Summary

  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

    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

  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

    Summary

  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

    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

  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

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

About the Authors

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. He has 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, FCCPjoined 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).

About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED071000
MEDICAL / Pharmacology
MED090000
MEDICAL / Biostatistics
REF000000
REFERENCE / General