Multiply robust estimation for causal survival analysis with treatment noncompliance

TL;DR

This paper introduces a multiply robust estimator for causal survival analysis in clinical trials with treatment noncompliance, providing consistent estimates even with some model misspecification, and applies it to the ADAPTABLE trial.

Contribution

It develops a novel multiply robust estimator for principal survival causal effects under noncompliance, addressing a key challenge in treatment effect estimation.

Findings

High-dose aspirin shows differential effects among patient subgroups.

The estimator remains consistent under certain model misspecifications.

Sensitivity analysis assesses robustness of causal conclusions.

Abstract

Comparative effectiveness research frequently addresses a time-to-event outcome and can require unique considerations in the presence of treatment noncompliance. Motivated by the challenges in addressing noncompliance in the ADAPTABLE pragmatic clinical trial, we develop a multiply robust estimator to estimate the principal survival causal effects under the principal ignorability and monotonicity. The multiply robust estimator is consistent even if one, and sometimes two, of the required models are misspecified. We apply the multiply robust method in the ADAPTABLE trial to evaluate the effect of low- versus high-dose aspirin assignment on patients' death and hospitalization from cardiovascular diseases. We find that, comparing to low-dose assignment, assignment to the high-dose leads to differential effects among always high-dose takers, compliers, and always low-dose takers. Such treatment effect heterogeneity contributes to the null intention-to-treatment effect. We further perform a formal sensitivity analysis for investigating the robustness of our causal conclusions under violation of two identification assumptions specific to noncompliance.