1st Edition

Introduction to Statistical Methods for Clinical Trials

Edited By Thomas D. Cook, David L. DeMets Copyright 2008
    464 Pages 4 B/W Illustrations
    by Chapman & Hall

    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.

    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
    Study Design
    Early Phase Trials
    Phase III/IV Trials
    Non-Inferiority Designs
    Screening, Prevention, and Therapeutic Designs
    Adaptive Designs
    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
    The Role of Randomization
    Fixed Randomization Procedures
    Treatment- and Response-Adaptive Randomization Procedures
    Covariate-Adaptive Randomization Procedures
    Summary and Recommendations
    Data Collection and Quality Control
    Planning for Collection of Clinical Trial Data
    Categories of Clinical Data
    Data Quality Control
    Survival Analysis
    Estimation of Survival Distributions
    Comparison of Survival Distributions
    Regression Models
    Composite Outcomes
    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
    Additional Levels of Clustering
    Generalized Estimating Equations for Non-Normal Data
    Missing Data
    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
    Data Monitoring and Interim Analysis
    Data and Safety Monitoring
    The Repeated Testing Problem
    Group Sequential Tests
    Triangular Test
    Curtailment Procedures
    Inference Following Sequential Tests
    Selected Issues in the Analysis
    Bias in the Analysis of Clinical Trial Data
    Choice of Analysis Population
    Missing Data
    Subgroup Analyses
    Multiple Testing Procedures
    Closeout and Reporting
    Closing out a Trial
    Reporting Trial Results
    Appendix: Delta Method, Maximum Likelihood Theory, and Information
    Delta Method
    Asymptotic Theory for Likelihood-Based Inference
    Hypothesis Testing
    Computing the MLE
    Brownian Motion


    Thomas D. Cook, David L. DeMets

    … There is much good material in this book. The individual chapters are well written and cover the technical aspects as well. A major strength is the ordering of topics to follow the thought process used in the development and implementation of a protocol from defining the question to reporting results. There are careful discussions on fundamental principles and the pivotal role played by statistics is well brought out. … there is much that practicing pharmaceutical statisticians will find useful in this book. They will find the coverage of fundamental principles useful and the technical content of the book a good reference source. …
    Pharmaceutical Statistics, 2010

    … fits the need for a contemporary text and handbook that is oriented toward the clinical trial statistician. I highly recommend it and look forward to using it as both a primary and supplemental text in our curriculum, as well as a research resource.
    —James J. Dignam, University of Chicago, JASA, March 2009

    The (technical) statistical content is the main focus of the book and this is what helps it to stand apart from most others on clinical trials (even the more obviously statistically orientated ones). It takes the reader to quite a technical background that would serve him or her well if moving on to research problems in the various areas covered, yet does not lose sight of practical issues. … For those of us with the interest (and need) to grapple with these more statistical issues, I wholeheartedly recommend it.
    Biometrics, December 2008

    …The book is very well written and clear. … the authors generally strike the right balance for the intended audience. The inclusion of many historically important as well as contemporary examples to illustrate various points throughout the text is a major strength, as is the inclusion of several modern topics not seen in other texts. As a basis for a course in clinical trials for graduate students in biostatistics, this book is outstanding. In addition, statisticians in the pharmaceutical industry, government, or academia … will find this text extremely informative and useful.”
    —Michael P. McDermott, University of Rochester Medical Center, Journal of Biopharmaceutical Statistics, 2008