Sample size estimation for stratified individual and cluster randomized trials with binary outcomes.

ABSTRACT: Individual randomized trials (IRTs) and cluster randomized trials (CRTs) with binary outcomes arise in a variety of settings and are often analyzed by logistic regression (fitted using generalized estimating equations for CRTs). The effect of stratification on the required sample size is less well understood for trials with binary outcomes than for continuous outcomes. We propose easy-to-use methods for sample size estimation for stratified IRTs and CRTs and demonstrate the use of these methods for a tuberculosis prevention CRT currently being planned. For both IRTs and CRTs, we also identify the ratio of the sample size for a stratified trial vs a comparably powered unstratified trial, allowing investigators to evaluate how stratification will affect the required sample size when planning

SUBMITTER: Kennedy-Shaffer L 

PROVIDER: S-EPMC7247053 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature