Estimation of treatment effects in presence of differential use of post-randomization concomitant medication with time-to-event outcomes

TL;DR

This paper introduces a causal inference framework using TMLE to estimate treatment effects in trials with differential post-randomization medication use, addressing confounding in time-to-event outcomes.

Contribution

It proposes a novel class of estimands and stochastic interventions to isolate treatment effects from concomitant medication use, with flexible adjustment for time-dependent covariates.

Findings

Method successfully applied to simulation data.

Application to LEADER trial illustrates practical utility.

Estimates provide clearer insight into treatment effects.

Abstract

In placebo-controlled randomized trials, the post-randomization use of concomitant medications may be higher in the placebo arm than in the treatment arm. This may dilute the full benefits of the randomized drug as estimated by the intention-to-treat analysis. We focus on cardiovascular outcomes trials in type-2 diabetes patients of glucose-lowering treatments where patients in the placebo arm are more likely to add other glucose-lowering agents with established cardio-protective properties. As a supplement to the intention-to-treat analysis, we propose a class of estimands within a causal framework that isolates the specific impact of the treatment being studied from that of concomitant treatment use. These estimands are defined under time-dependent treatment interventions to balance exposure to additional medications across intervention arms. We advocate for specific stochastic interventions to achieve this balance while minimizing positivity violations, which arise when certain treatment combinations or characteristics are not sufficiently represented in the data. We employ targeted minimum loss-based estimation (TMLE) to optimize the estimation procedure for our estimands while allowing for flexible adjustments for time-dependent covariates from follow-up visits. Finally, we demonstrate the application of the methods through a simulation study and a real-world example from the LEADER cardiovascular outcomes trial, which assessed cardiovascular risk for liraglutide versus placebo.