Simple Estimation of Patient-Oriented Effects from Randomized Trials: An Open and Shut CACE

Abstract

In randomized controlled trials, the intention-to-treat estimator provides an unbiased estimate of the causal effect of treatment assignment on the outcome. However, patients often want to know what the effect would be if they were to take the treatment as prescribed (the patient-oriented effect), and several researchers have suggested that the more relevant causal effect for this question is the complier average causal effect (CACE), also referred to as the local average treatment effect. Sophisticated approaches to estimating the CACE include Bayesian and frequentist methods for principal stratification, inverse-probability-of-treatment-weighted estimators, and instrumental-variable (IV) analysis. All of these approaches exploit information about adherence to assigned treatment to improve upon the intention-to-treat estimator, but they are rarely used in practice, probably because of their complexity. The IV principal stratification estimator is simple to implement but has had limited use in practice, possibly due to lack of familiarity. Here, we show that the IV principal stratification estimator is a modified per-protocol estimator that should be obtainable from any randomized controlled trial, and we provide a closed form for its robust variance (and its uncertainty). Finally, we illustrate sensitivity analyses we conducted to assess inference in light of potential violations of the exclusion restriction assumption.