Bayesian Sample-Size Determination for Longitudinal Trials With Attrition: The <i>BayesSSD</i> Package

Abstract

Sample-size determination (SSD) is the procedure of determining the number of subjects necessary to achieve a desired level of statistical power and is essential in planning an experiment. Although open-access software for SSD via closed-form equations is readily available in the null-hypothesis-significance-testing framework, this approach has been subject to severe criticism in the past. As an alternative, Bayesian evaluation of informative hypotheses via the Bayes’s factor or posterior model probabilities has been proposed. However, available software packages for Bayesian SSD are either (a) limited to simpler models, such as analysis of variance and t test, and cannot handle longitudinal data or (b) unable to handle more than two treatment conditions. Current software also neglects participant attrition—a common occurrence that may substantially reduce the power of a longitudinal experiment. In the present work, we address this gap by introducing the open-access R package BayesSSD , which performs simulation-based Bayesian SSD for longitudinal trials with two or more treatment conditions. Through a simulation study, we show that not only the proportion of individuals dropping out but also the timing of dropout need to be considered when performing SSD. In the presented method, various patterns of expected attrition can be specified via parametric and nonparametric survival functions and accounted for in the SSD procedure. To facilitate adoption, we provide a tutorial with empirical data, illustrating each step of the Bayesian SSD process.