Analysis of Data with Imbalance in the Baseline Outcome Variable for Randomized Clinical Trials

Abstract

In randomized clinical trials, substantial imbalance in the baseline outcome variable may occur by chance. Inference on treatment effect may be confounded by such imbalance if not properly accounted for. The usual unadjusted analysis may be conditionally biased with an inflated Type I error rate or reduced power conditioning on the baseline imbalance in the observed sample. This paper reviews methods for baseline adjustment with emphasis on the analysis of co-variance (ANCOVA) model with the baseline outcome as a covariate. Many issues on the ANCOVA model, including the nature of the adjustment, unconditional and conditional properties of treatment effect estimate from the ANCOVA, and baseline with measurement errors are discussed. In summary, the ANCOVA model with the baseline outcome as a covariate is more efficient unconditionally, and has better statistical properties conditionally, than the usual analysis of variance (ANOVA) analysis based on either posttreatment outcome or change from baseline and, therefore, is recommended. A real-life data set is analyzed and used to illustrate the unconditional and conditional properties.