An Example to Illustrate Randomized Trial Estimands and Estimators

Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; namely the treatment policy, composite variable, while on treatment, hypothetical and principal stratum estimands. We describe an illustrative example to elucidate the difference between these five strategies for handling intercurrent events and provide an estimation technique for each. Specifically, we consider the intercurrent event of treatment discontinuation and introduce potential outcome notation to describe five estimands and corresponding estimators: 1) an intention-to-treat estimator of the total effect of a treatment policy; 2) an intention-to-treat estimator of a composite of the outcome and remaining on treatment; 3) a per-protocol estimator of the outcome in individuals observed to remain on treatment; 4) a g-computation estimator of a hypothetical scenario that all individuals remain on treatment; and 5) a principal stratum estimator of the treatment effect in individuals who would remain on treatment under the experimental condition. Additional insight is provided by defining situations where certain estimands are equal, and by studying the while on treatment strategy under repeated outcome measures. We highlight relevant causal inference literature to enable adoption in practice.