Power and Sample Size in R

Power and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software. It is designed to be used as a learning tool for students as well as a resource for experienced statisticians and investigators.

The book begins by explaining the process of power calculation step by step at an introductory level and then builds to increasingly complex and varied topics. For each type of study design, the information needed to perform a calculation and the factors that affect power are explained. Concepts are explained with statistical rigor but made accessible through intuition and examples. Practical advice for performing sample size and power calculations for real studies is given throughout.

The book demonstrates calculations in R. It is integrated with the companion R package powertools and also draws on and summarizes the capabilities of other R packages. Only a basic proficiency in R is assumed.

Topics include comparison of group means and proportions; ANOVA, including multiple comparisons; power for confidence intervals; multistage designs; linear, logistic and Poisson regression; crossover studies; multicenter, cluster randomized and stepped wedge designs; and time to event outcomes. Chapters are also devoted to designing noninferiority, superiority by a margin and equivalence studies and handling multiple primary endpoints.

By emphasizing statistical thinking about the factors that influence power for different study designs and outcomes as well as providing R code, this book equips the reader with the knowledge and tools to perform their own calculations with confidence. Supplemental material available at: https://powerandsamplesize.org/.

Key Features:

• Explains power and sample size calculation for a wide variety of study designs and outcomes
• Suitable for both students and experienced researchers
• Highlights key factors influencing power and provides practical tips for designing real studies
• Includes extensive examples with R code

Catherine M. Crespi is Professor in the Department of Biostatistics at the Jonathan and Karin Fielding School of Public Health, University of California Los Angeles. Her areas of specialization include trial design and analysis, multilevel modelling, longitudinal data and multivariate statistics. She is a Fellow of the American Statistical Association and an officer of the International Biometric Society.