Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials

; John Balser ; Jim Roach ; Robin Bliss

"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. Les mer
Vår pris

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Legg i
Legg i
Vår pris: 725,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

"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

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


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


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


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


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


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


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


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


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


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


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


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


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