Title: How to correct for baseline covariates in longitudinal clinical trials
Authors: Geert Verbeke - KU Leuven (Belgium) [presenting]
Abstract: In clinical trials, mixed models are becoming more popular for the analysis of longitudinal data. The main motivation is often expected dropout, which can easily be handled by analysing the longitudinal trajectories. In many situations, analyses are corrected for baseline covariates such as study site or stratification variables. Key questions are then how to perform a longitudinal analysis correcting for baseline covariates, and how sensitive are the results with respect to choices made and models used. We will first present and compare a number of techniques available to correct for baseline covariates within the context of the linear mixed model for continuous outcomes. Second, we will study the sensitivity of the various techniques in case the baseline correction is based on a wrong model or does not include important covariates. Finally, our findings will be used to formulate some general guidelines relevant in a clinical trial context. All findings and results will be illustrated extensively using data from a real clinical trial.