Estimating treatment effects with competing intercurrent events in randomized controlled trials

Thomas M. Menino Convention & Exhibition Center 

The analysis of randomized controlled trials is often complicated by intercurrent events (IEs), which occur after treatment initiation and affect the interpretation or existence of outcome measurements, such as treatment discontinuation or additional medication use. In two recent clinical trials for systemic lupus erythematosus, we classify IEs into effect-informative and effect-uninformative categories. To define a clinically meaningful estimand, we adopt tailored strategies for each type. For effect-informative IEs, we use a composite strategy that assigns an outcome reflecting treatment failure. For effect-uninformative IEs, we apply a hypothetical strategy, assuming their timing is conditionally independent of the outcome given treatment and baseline covariates. We further address competing IEs, where the first event censors subsequent ones. We develop a unified framework for estimand formulation, nonparametric identification, and semiparametric estimation, and propose weighting, outcome regression, and doubly robust estimators. Applying our methods to the two trials demonstrates robustness and practical value.

Causal inference

Clinical trial

International Council for Harmonization

Post-treatment variable

Potential outcomes 

Biopharmaceutical Section