Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial.

After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals.

Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.

Table of Contents

PREFACE

Introduction to Clinical Trials

History and Background

Ethics of Clinical Research

Types of Research Design and Types of Trials

The Need for Clinical Trials

The Randomization Principle

Timing of a Clinical Trial

Trial Organization

Protocol and Manual of Operations

Regulatory Issues

Overview of the Book

Defining the Question

Statistical Framework

Elements of Study Question

Outcome or Response Measures

The Surrogate Outcome

Composite Outcomes

Summary

Problems

Study Design

Early Phase Trials

Phase III/IV Trials

Non-Inferiority Designs

Screening, Prevention, and Therapeutic Designs

Adaptive Designs

Conclusions

Problems

Sample Size

Sample Size versus Information

A General Setup for Frequentist Designs

Loss to Follow-up and Non-Adherence

Survival Data

Clustered Data

Tests for Interaction

Equivalence/Non-Inferiority Trials

Other Considerations

Problems

Randomization

The Role of Randomization

Fixed Randomization Procedures

Treatment- and Response-Adaptive Randomization Procedures

Covariate-Adaptive Randomization Procedures

Summary and Recommendations

Problems

Data Collection and Quality Control

Planning for Collection of Clinical Trial Data

Categories of Clinical Data

Data Quality Control

Conclusions

Survival Analysis

Background

Estimation of Survival Distributions

Comparison of Survival Distributions

Regression Models

Composite Outcomes

Summary

Problems

Longitudinal Data

A Clinical Longitudinal Data Example

The Subject-Specific Model

Two-Stage Estimation

The Random-Effects, Subject-Specific Model

The Population-Average (Marginal) Model

Restricted Maximum Likelihood Estimation (REML)

Standard Errors

Testing

Additional Levels of Clustering

Generalized Estimating Equations for Non-Normal Data

Missing Data

Summary

Quality of Life

Defining QoL

Types of QoL Assessments

Selecting a QoL Instrument

Developing a QoL Instrument

Quality of Life Data

Analysis of QoL Data

Summary

Data Monitoring and Interim Analysis

Data and Safety Monitoring

Examples

The Repeated Testing Problem

Group Sequential Tests

Triangular Test

Curtailment Procedures

Inference Following Sequential Tests

Discussion

Problems

Selected Issues in the Analysis

Bias in the Analysis of Clinical Trial Data

Choice of Analysis Population

Missing Data

Subgroup Analyses

Multiple Testing Procedures

Summary

Problems

Closeout and Reporting

Closing out a Trial

Reporting Trial Results

Problems

Appendix: Delta Method, Maximum Likelihood Theory, and Information

Delta Method

Asymptotic Theory for Likelihood-Based Inference

Hypothesis Testing

Computing the MLE

Information

Brownian Motion

REFERENCES

INDEX

Foundations of Program Evaluation heralds a thorough exploration of the field of program evaluation--looking back on its origins. By summarizing, comparing, and contrasting the work of seven major theorists of program evaluation, this book provides an important perspective on the current state of evaluation theory and provides suggestions for ways of improving its practice. Beginning in Chapter Two, the authors develop a conceptual framework to analyze how successfully each theory meets the specific criteria of its framework. Each subsequent chapter is devoted to the presentation of the theoretical and practical advice of a significant theorist--Michael Scriven, Donald Campbell, Carol Weiss, Joseph Wholey, Robert Stake, Lee Cronbach, and Peter Rossi.

Meta-Analysis for Explanation brings exemplary illustrations of research synthesis together with expert discussion of the use of meta-analytic techniques. Four meta-analyses prepared by Betsy Jane Becker, Elizabeth C. Devine, Mark W. Lipsey, and William R. Shadish reflect the variety of techniques available to quantitative reviewers. Framing these, chapters written by the authors offer a general overview of the field and a discussion of the scientific value of meta-analysis.