Causal survival analysis: A guide to estimating intention-to-treat and per-protocol effects from randomized clinical trials with non-adherence

Abstract

When reporting results from randomized experiments, researchers often choose to present a per-protocol effect in addition to an intention-to-treat effect. However, these per-protocol effects are often described retrospectively, for example, comparing outcomes among individuals who adhered to their assigned treatment strategy throughout the study. This retrospective definition of a per-protocol effect is often confounded and cannot be interpreted causally because it encounters treatment-confounder feedback loops, where past confounders affect future treatment, and current treatment affects future confounders. Per-protocol effects estimated using this method are highly susceptible to the placebo paradox, also called the “healthy adherers” bias, where individuals who adhere to placebo appear to have better survival than those who don’t. This result is generally not due to a benefit of placebo, but rather is most often the result of uncontrolled confounding. Here, we aim to provide an overview to causal inference for survival outcomes with time-varying exposures for static interventions using inverse probability weighting. The basic concepts described here can also apply to other types of exposure strategies, although these may require additional design or analytic considerations. We provide a workshop guide with solutions manual, fully reproducible R, SAS, and Stata code, and a simulated dataset on a GitHub repository for the reader to explore.