Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods

Abstract

Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVA-CHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward-Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.

Keywords: Analysis of covariance; Analysis of variance; Linear mixed model; Pre-post; Repeated measures; Rrandomized trial.

Figures

Figure 1:

Distribution of treatment effects estimates varied by correlation, sample size and true positive β1 values under Y0~N(0,1). Boxplots for parameter estimates for the 1000 simulations for the combinations of β1, n, and ρ are displayed in Figure 1 Consistent with the data tables, all parameter estimates are unbiased, and the boxplots highlight differences in variability for the models. In general, variance was much larger for small values of ρ and small n. ANCOVA models have smaller variances compared to ANOVA and LMM, though differences are quite small.