Addressing Missing Data in Clinical Trials With a Hybrid Control Arm

Randomized trials have long been the standard for evaluating experimental treatments in medical and pharmacological studies, but the associated time and cost have spurred research into designs that augment randomized studies with data from external sources. Hybrid control arms offer the promise of improved precision-at the possible expense of bias-by forming a control arm from patients randomized to control along with historical controls or patients found in a registry. Although recent reviews have published guidance on minimizing and addressing missing data in clinical trials, the issue has received little attention in the context of trials with a hybrid control arm. We propose novel treatment effect estimators that handle missing outcomes in both the trial and external control datasets, even when the missingness patterns differ between them. These estimators are built from augmented weighted estimating equations and are doubly robust when outcomes are missing at random (MAR). In addition, we introduce a sensitivity analysis using a pattern-mixture model to assess robustness of the effect estimates under violations of the MAR assumption. We compare these estimators to a popular parametric modeling technique through simulation and an application to a real-world trial.