Power and Sample Size for Longitudinal Models in R - The longpower Package and Shiny App.

Abstract

Longitudinal studies are ubiquitous in medical and clinical research. Sample size computations are critical to ensure that these studies are sufficiently powered to provide reliable and valid inferences. There are several methodologies for calculating sample sizes for longitudinal studies that depend on many considerations including the study design features, outcome type and distribution, and proposed analytical methods. We briefly review the literature and describe sample size formulas for continuous longitudinal data. We then apply the methods using example studies comparing treatment versus control groups in randomized trials assessing treatment effect on clinical outcomes. We also introduce a Shiny app that we developed to assist researchers with obtaining required sample sizes for longitudinal studies by allowing users to enter required pilot estimates. For Alzheimer's studies, the app can estimate required pilot parameters using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Illustrative examples are used to demonstrate how the package and app can be used to generate sample size and power curves. The package and app are designed to help researchers easily assess the operating characteristics of study designs for Alzheimer's clinical trials and other research studies with longitudinal continuous outcomes. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).