This book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials.

Part I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided.

The book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable forbeginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.

Autorentext

Prof. Dr. Meinhard Kieser is a Professor of Medical Biometry and Director of the Institute of Medical Biometry and Informatics at the University of Heidelberg. He studied Mathematics and received his PhD in Medical Biometry in 1992. He then worked for more than 15 years as a biostatistician and Head of Biometrics in the pharmaceutical industry. Professor Kieser has comprehensive experience in the planning and analysis of clinical trials and has been a member of numerous independent data monitoring committees. He serves as an associate editor for Pharmaceutical Statistics and the Journal of Biopharmaceutical Statistics. His main research areas are biostatistical methods for clinical trials, including sample size calculation and recalculation, and methods for evidence synthesis.

Inhalt

Part I Basics

1        Introduction

1.1       Background and outline

1.2       Examples

1.2.1        The ChroPac trial

1.2.2        The Parkinson trial

1.3       General considerations when calculating sample sizes

2        Statistical test and sample size calculation

2.1       The main principle of statistical testing 2.2       The main principle of sample size calculation

Part II Sample size calculation

3        Comparison of two groups for normally distributed outcomes and test for difference or superiority

3.1       Background and notation

3.2       z-test

3.3       t-test

3.4       Analysis of covariance

3.5       Bayesian approach

3.5.1        Background

3.5.2        Methods  

4        Comparison of two groups for continuous and ordered categorical outcomes and test for difference or superiority

4.1       Background and notation

4.2       Continuous outcomes

4.3       Ordered categorical outcomes

4.3.1        Assumption-free approach

4.3.2        Assuming proportional odds

5        Comparison of two groups for binary outcomes and test for difference and superiority 5.1       Background and notation

5.2       Asymptotic tests

5.2.1        Difference of rates as effect measure 5.2.2        Risk ratio as effect measure

5.2.3        Odds ratio as effect measure

5.2.4        Logistic regression 5.3       Exact unconditional tests

5.3.1        Background

5.3.2        Fisher-Boschloo test  

6        Comparison of two groups for time-to-event outcomes and test for differences or superiority

6.1       Background and notation

6.1.1        Time-to-event data

6.1.2        Sample size calculation for time-to-event data 6.2       Expone...