Handling Missing Data in Participants with Baseline but No Post‐Baseline Data

Participants who are randomized to treatment but have no post-baseline data pose a unique challenge. These participants need to be included to preserve randomization. Because there is no information about the outcome or the intercurrent event(s) that led to missing data, an estimand of interest, and the focus of this study, is a hypothetical strategy to estimate what would have been observed if participants had not discontinued. Various imputation-based and likelihood-based analyses were compared in simulated and real clinical trial data. Models that used baseline as a covariate or constrained baseline values to be equal yielded similar results and had greater power than an unconstrained analysis that fit baseline as a response. Assigning change to the first post-baseline visit as 0 and applying an analysis with baseline as a covariate controlled Type I error at the nominal level and had power equal to or greater than other methods. Treatment contrasts were not biased when the reason for missing all post-baseline data was random or treatment related. Within-group bias occurred with outcome-related missingness of all post-baseline data, but the bias was nearly equal in the two arms, leading to unbiased treatment contrasts. Bias occurred when missing all post-baseline data was related to treatment and outcome. Given the idiosyncratic nature of clinical trials, no universally best analytic approach exists for dealing with participants that have a baseline but no post-baseline data. Analysts can choose among the methods to tailor an approach to the situation at hand.