The primary analysis in a longitudinal randomized controlled trial is sometimes a comparison of arms at a single time point. While a two-sample t-test is often used, missing data are common in longitudinal studies and decreases power by reducing sample size. Mixed models for repeated measures (MMRM) can test treatment effects at specific time points, have been shown to give unbiased estimates in certain missing data contexts, and may be more powerful than a two sample t-test.
Ms. Erin L. Ashbeck, a graduate student in biostatistics at the University of Arizona Mel and Enid Zuckerman College of Public Health and a colleague conducted a simulation study to compare the performance of a complete-case t-test to a MMRM in terms of power and bias under different missing data mechanisms. Impact of within- and between-person variance, dropout mechanism, and variance-covariance structure were all considered. The study was published in the journal BMC Medical Research Methodology.
While both complete-case t-test and MMRM provided unbiased estimation of treatment differences when data were missing completely at random, MMRM yielded an absolute power gain of up to 12 percent. The MMRM provided up to 25 percent absolute increased power over the t-test when data were missing at random, as well as unbiased estimation.
Investigators interested in single time point comparisons should use a MMRM with a contrast to gain power and unbiased estimation of treatment effects instead of a complete-case two sample t-test.
BMC Medical Research Methodology