About the Book

Confidence Intervals for Discrete Data in Clinical Research is designed as a toolbox for biomedical researchers. Analysis of discrete data is one of the most used yet vexing areas in clinical research. The array of methodologies available in the literature to address the inferential questions for binomial and multinomial data can be a double-edged sword. On the one hand, these methods open a rich avenue of exploration of data; on the other, the wide-ranging and competing methodologies potentially lead to conflicting inferences, adding to researchers' confusion and frustration and also leading to reporting bias. This book addresses the problems that many practitioners experience in choosing and implementing fit for purpose data analysis methods to answer critical inferential questions for binomial and count data.

This book is an outgrowth of the authors' collective experience in biomedical research and provides an excellent overview of inferential questions of interest for binomial proportions and rates based on count data, and reviews various solutions to these problems available in the literature. Each chapter discusses the strengths and weaknesses of the methods and suggests practical recommendations. The book's primary focus is on applications in clinical research, and the goal is to provide direct benefit to the users involved in the biomedical field. The data analysis methods are illustrated with examples from clinical trials, and the analytical approaches discussed in the book are consistent with regulatory guidelines of clinical research. The inferential procedures are demonstrated with software packages SAS, StatXact PROCs, and R, making the methodologies easily accessible to a broad audience.

The key features:

• The book is a definitive reference of wide-ranging methods for the analysis of binomial, multinomial and count data.
• Each chapter provides detailed comparisons of the methods using multiple metrics and includes recommendations of the best practices for data analysis.
• The book illustrates the methods with real world examples from clinical trials pertinent to practitioners and researchers.
• Each chapter includes detailed SAS/R codes allowing readers a straightforward pathway to all methodologies discussed in the book.