Abstract

The draft ICH E9 (R1) addendum by the International Conference on Harmonisation working group opens for the use of a principal stratum (PS) in the analysis of study data for regulatory purpose, if a relevant estimand can be justified. Inspired by the so-called complier average causal effect and work within this framework, we propose a new estimator—Extrapolation based on propensity to comply—that estimates the treatment effect of an active treatment A relative to a comparator B (active or placebo), in the PS of patients who would comply, if they were treated with treatment A. Sensitivity of the approach to the number of covariates and their ability to predict PS membership is investigated based on data from a placebo-controlled study of brexpiprazole in schizophrenia. The performance of the estimator is compared with another estimator that is also based on principal stratification. A simulation study supports that the proposed estimator has a negligible bias even with a small sample size, except when the covariate predicting compliance is very weak. Not surprisingly, precision of the estimate increases substantially with stronger predictors of compliance. It is shown analytically that the estimator used for comparison is biased in general, and the bias is explicitly derived in a simple case with a binary predictor of compliance. While the proposed methodology is technically easy to implement, choosing predictors for modeling compliance is a key issue in practice.

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