Here are three books on Statistical Methods for analyzing data coming from Clinical Trials, and a short review.
Statistics Applied to Clinical Trials, T.J. Cleophas, A.H. Zwinderman, T.F. Cleophas, and E.P. Cleophas (Springer, 2009)
In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to little sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were being recognized and, subsequently were better accounted for: carryover effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses etc. Such flaws mainly of a technical nature have been largely implemented and lead to trials after 1970 being of significantly better quality than before. The past decade focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial organs, including ethic committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. The present book not only explains classical statistical analyses of clinical trials, but also addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, meta-analyses, and provides a framework of the best statistical methods currently available for such purpose. This book is not only useful for investigators involved in the field of clinical trials, but also for students and physicians who wish to better understand the data of trials as published currently.
Maybe my preferred textbook because it is composed of a lot of chapters (47 in the fourth edition) that cover a broad range of different issues that all have in common that they concentrate on specific aspects on statistical methods when analyzing and interpreting results from RCTs. As such, this textbook don’t follow a logical sequence from design and randomization, data collection, analysis, and reporting. It merely addresses various facets of statistical methodology for those doing clinical research or having to interpret their findings. I would say it is much in the spirit of Statistical Issues in Drug Development, by Stephen S. Senn (Wiley, 2008).
Clinical Trials, A Methodological Perspective, Steven Piantadosi (Wiley, 2005)
The author bases the revisions and updates on his own classroom experience, as well as feedback from students, instructors, and medical and statistical professionals involved in clinical trials. The Second Edition greatly expands its coverage, ranging from statistical principles to controversial topics, including alternative medicine and ethics. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First Edition will discover completely new chapters, including: Contexts for clinical trials, Statistical perspectives, Translational clinical trials, Dose-finding and dose-ranging designs. Each chapter is accompanied by a summary to reinforce the key points. Revised discussion questions stimulate critical thinking and help readers understand how they can apply their newfound knowledge, and updated references are provided to direct readers to the most recent literature. This text distinguishes itself with its accessible and broad coverage of statistical design methods–the crucial building blocks of clinical trials and medical research. Readers learn to conduct clinical trials that produce valid qualitative results backed by rigorous statistical methods.
This is a 500+ pages textbook which covers the basic principles of experimental design applied to clinical trials, and their statistical analysis. In fact, the first four chapters are devoted to clinical trials per se: RCTs as a scientific and well-founded of asserting the effect of treatments or drugs, some useful digressions about ethics, and an overview of the main use of RCTs (drug development, preventive medicine, complementary and alternative medicine, surgery, screening, and diagnostic trials). Chapter 5 is a review of basic statistical principle, with a discussion of the frequentist and bayesian approaches to statistical inference. In this respect, I always found that David Clayton made a very good job in his book, Statistical Models in Epidemiology (Oxford, 1993), based on the principle of Likelihood. Chapter 6 and 7 are more concerned with statistical design: planning (including specific experimental plans, like equivalence and non-inferiority trials; factorial and crossover designs are treated in chapters 19 and 20), source of errors, and clinical vs. statistical biases. Considerations about power and sample size would have fit near here, but they are summarized in an entire chapter, for they are discussed at length in different designs (safety and activity studies, comparative trials, and ES trials). As we know, randomization techniques are an important aspect for allocating patients to treatment in a way that will ensure safe conclusions: it is the purpose of chapter 13. Chapters 16 and 17 deal with the study of prognostic factors, while chapter 21 is about meta-analysis.
Design and Analysis of Clinical Trials: Concepts and Methodologies, S.-C. Chow and J.-P. Liu (Wiley, 2004)
Design and Analysis of Clinical Trials, Second Edition provides both a comprehensive, unified presentation of principles and methodologies for various clinical trials, and a well-balanced summary of current regulatory requirements. This unique resource bridges the gap between clinical and statistical disciplines, covering both fields in a lucid and accessible manner. Thoroughly updated from its first edition, the Second Edition of Design and Analysis of Clinical Trials features new topics such as: Clinical trials and regulations, especially those of the ICH, Clinical significance, reproducibility, and generalizability, Goals of clinical trials and target population, New study designs and trial types, Sample size determination on equivalence and noninferiority trials, as well as comparing variabilities . Also, three entirely new chapters cover: Designs for cancer clinical trials, Preparation and implementation of a clinical protocol, Data management of a clinical trial. Written with the practitioner in mind, the presentation assumes only a minimal mathematical and statistical background for its reader. Instead, the writing emphasizes real-life examples and illustrations from clinical case studies, as well as numerous references-280 of them new to the Second Edition-to the literature. Design and Analysis of Clinical Trials, Second Edition will benefit academic, pharmaceutical, medical, and regulatory scientists/researchers, statisticians, and graduate-level students in these areas by serving as a useful, thorough reference source for clinical research.