Power difference in a χ² test vs generalized linear mixed model in the presence of missing data - a simulation study.

ABSTRACT:

Background

Longitudinal randomized controlled trials (RCTs) often aim to test and measure the effect of treatment between arms at a single time point. A two-sample χ² test is a common statistical approach when outcome data are binary. However, only complete outcomes are used in the analysis. Missing responses are common in longitudinal RCTs and by only analyzing complete data, power may be reduced and estimates could be biased. Generalized linear mixed models (GLMM) with a random intercept can be used to test and estimate the treatment effect, which may increase power and reduce bias.

Methods

We simulated longitudinal binary RCT data to compare the performance of a complete case χ2 test to a GLMM in terms of power, type I error, relative bias, and coverage under d

SUBMITTER: Miller ML 

PROVIDER: S-EPMC7053142 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature