In randomized trials, the most commonly reported method of effect estimation is intention-to-treat (ITT), and to a lesser extent the per-protocol. The ITT is preferred because it is an unbiased estimator of the effect of treatment assignment. However, if there is any non-adherence the ITT is a biased estimate of the treatment effect, defined as the contrast between the potential outcome if treated versus the potential outcome if not treated. The treatment effect is most relevant to patients. Principal stratication is a framework for estimating treatment effects that combines potential outcomes and latent adherence strata. It yields an unbiased estimator of the complier average causal effect (CACE) for a difference in means or proportions, in the setting of all-or-nothing adherence. This paper addresses estimation of the causal hazard ratio for the compliers in a setting of right censoring of a time-to-event. We propose a novel approach to operationalizing principal stratification using weights. We report the results of simulations that vary the amount of adherence and selection bias that show the hazard ratio estimators we propose have minimal bias compared to the ITT, and per-protocol estimators. We demonstrate the approach using a population-based randomized controlled trial of colorectal cancer screening subject to a high frequency of nonadherence in the screening arm.
April 24, 2016