Preface

Summary

This book focuses on the prevention and treatment of missing data in longitudinal clinical trials with repeated measures, such as are common in later phases of medical research and drug development. Recent decades have brought advances in statistical theory, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. A danger in such circumstances is to blindly use newer methods without proper understanding of their strengths and limitations, or to disregard all newer methods in favor of familiar approaches.

Moreover, the complex discussions on how to analyze incomplete data have overshadowed discussions on ways to prevent missing data, which would of course be the preferred solution. Therefore, preventing missing data through appropriate trial design and conduct is given significant attention in this book. Nevertheless, despite all efforts at prevention, missing data will remain an ever-present problem and analytic approaches will continue to be an important consideration.

Recent research has fostered an emerging consensus regarding the analysis of incomplete longitudinal data. Key principles and analytic methods are explained in terms non-statisticians can understand. Although the use of equations, symbols, and Greek letters to describe the analyses is largely avoided, sufficient technical detail is provided so readers can take away more than a peripheral understanding of the methods and issues. For those with in-depth statistical interests, reference to more technical literature is provided.