Statistical Design and Analysis of Clinical Trials: Principles and Methods, 1st Edition (Hardback) book cover

Statistical Design and Analysis of Clinical Trials

Principles and Methods, 1st Edition

By Weichung Joe Shih, Joseph Aisner

Chapman and Hall/CRC

244 pages | 17 B/W Illus.

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Hardback: 9781482250497
pub: 2015-07-23
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Description

Statistical Design and Analysis of Clinical Trials: Principles and Methods concentrates on the biostatistics component of clinical trials. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 15 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods.

Teach Your Students How to Design, Monitor, and Analyze Clinical Trials

The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, explain the concept of different missing data processes, and describe how to analyze incomplete data by proper multiple imputations.

Turn Your Students into Better Clinical Trial Investigators

This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students a multidisciplinary understanding of the concepts and techniques involved in designing and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students in (bio)statistics, epidemiology, medicine, pharmacy, and public health.

Table of Contents

Overview

What Is a Clinical Trial?

Requirements for a Good Experiment

Ethics and Safety First

Classifications of Clinical Trials

Multidisciplinary Teamwork in Clinical Trials

Appendix 1.1: Elements of Informed Consent

Concepts and Methods of Statistical Designs

External Validity

Internal Validity

Repeatability

The Phenomenon of Regression toward the Mean and Importance of a Concurrent Control Group

Random Samples and Randomization of Samples

Methods for Randomization

Minimization

Table of Patient Demographics and Baseline Characteristics

Efficiency with Trade-Offs and Crossover Designs

Statistical Efficiency of a Design

Crossover Designs

Analysis of 2 × 2 Crossover Designs

Appendix 3.1: Efficiency of the 1:1 Allocation Assuming Equal Variance

Appendix 3.2: Optimal Allocation under Unequal Variance

Appendix 3.3: Optimizing Number of Responders

Sample Size and Power Calculations

Fundamentals

Comparing Means for Continuous Outcomes

Comparing Proportions for Binary Outcomes

Comparing Time-to-Event (Survival) Endpoints

Clustered (or Correlated) Observations

Sample Size for Testing a Noninferiority or Equivalence Hypothesis

Comparing Ordinal Endpoints by Wilcoxon–Mann–Whitney Test

Sample Size Adjustments

Sample Size by Simulation and Bootstrap

Appendix 4.1: Fundamentals of Survival Data Analysis

Appendix 4.2: Exponential Distribution Model

Appendix 4.3: Survival with Independent Censoring

Appendix 4.4: MLE with Censoring under the Exponential Model

Analysis of Covariance and Stratified Analysis

Principles of Data Analysis

Continuous Response—ANOVA and ANCOVA

Variance Reduction by Covariates

Stratified Analysis

Appendix 5.1: Weekly Average Pain Score Data

Sequential Designs and Methods—Part I: Expected Sample Size and Two-Stage Phase II Trials in Oncology

Maximum Sample Size and Expected Sample Size

One-Stage versus Two-Stage Cancer Phase II Trials

Simon’s Two-Stage Designs

Discussion

Sequential Designs and Methods—Part II: Monitoring Safety and Futility

Monitoring Safety

Monitoring Futility with Conditional Probability

Appendix 7.1: R Function for Obtaining Parameters of Prior Distribution Beta(a, b) Based on Method A

Appendix 7.2: R Function for Obtaining Parameters of Prior Distribution Beta(a, b) Based on Method B

Appendix 7.3: Notes on the Two-Stage Monitoring Process

Sequential Designs and Methods—Part III: Classical Group Sequential Trials

Regulatory Requirements and Logistical Considerations for Trial Monitoring

Statistical Methods

Power, Information, and Drift Parameter

P-Value When Trial Is Stopped

Estimation of Treatment Effect

Appendix 8.1: R Function qfind for Calculating the Critical Value (Boundary) of the Second (Final) Analysis

Appendix 8.2: A Further Note on the Partial Sum Process with Independent Increments

Appendix 8.3: Information Time/Fraction and Maximum-Duration Trial versus Maximum-Information Trial

Monitoring the Maximum Information

Sample Size Reestimation

Monitoring Trial Duration for Studies with Survival Endpoints

Modification of the Classical GS Alpha-Spending Function Procedure

Adaptive GS Procedure—Change Not Dependent on Unblinded Interim Data

Appendix 9.1

Appendix 9.2

Missing Data

Introduction

Question to Answer: Causal Estimand

Missing Data Patterns and Mechanisms

Ignorability and Nonignorability of Missing Data

Analysis under the MAR Assumption by Multiple Imputation

Analysis of Longitudinal Data with Monotone Pattern Missing Values under MAR

Analysis under a Particular NMAR Model Assumption by MI

Use Reason for Withdrawal and Follow-Up Time to Form the Missing Data Pattern and Sensitivity Analyses

Other NMAR Approaches

Appendix 10.1: Sampling Distribution Inference

Appendix 10.2: Likelihood Inference

Appendix 10.3: Bayesian Inference

Appendix 10.4: Equivalence between Selection Model and Pattern-Mixture Model for MCAR and MAR

Appendix 10.5: NFD Missingness Mechanism as a Subclass of NMAR for Longitudinal Data with Monotone Missing Data Pattern

Appendix 10.6: Equivalence between the Selection Model NFD Missingness Condition (Equation 10A.10) and the Pattern-Mixture Model NFD Missingness Condition (Equation 10A.12)

Homework Problems and References are included at the end of each chapter.

About the Authors

Weichung Joe Shih, PhD, is professor and chair of the Department of Biostatistics in the Rutgers School of Public Health at Rutgers University, and director of the Biometrics Division at the Rutgers Cancer Institute of New Jersey. He is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. He served on the advisory board of the U.S. FDA for reviewing new drug applications and was associate editor of professional journals, including Statistics in Medicine, Controlled Clinical Trials, Clinical Cancer Research, Statistics in Biopharmaceutical Research, and Statistics in Bioscience. His research interests include adaptive designs and missing data issues.

Joseph Aisner, MD, is a professor of medicine and a professor of environmental and occupational medicine at the Robert Wood Johnson Medical School of Rutgers University, director of the Medical Oncology Unit at the Robert Wood Johnson University Hospital, and co-leader of the Clinical Investigations Program at the Rutgers Cancer Institute of New Jersey. He is a fellow of the American College of Physicians and the American Society of Clinical Oncology. He serves on and chairs several National Data Monitoring Committees and has served on the editorial board of multiple journals, including Journal of Clinical Oncology, Cancer Therapeutics, Medical Oncology, Clinical Cancer Research, and Hematology-Oncology Today. His research interests include cancer clinical trials and evaluation of therapeutic interventions.

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