Doubly Robust Targeted Estimation of Conditional Average Treatment Effects for Time-to-event Outcomes with Competing Risks

TL;DR

This paper introduces a novel, efficient, and robust method for estimating conditional treatment effects in time-to-event data with competing risks, enhancing personalized treatment strategies in precision medicine.

Contribution

It derives the efficient influence function for CATE, develops a flexible TMLE framework, and introduces variable importance measures for treatment effect heterogeneity.

Findings

The method achieves asymptotic efficiency and double robustness.

Simulations confirm theoretical properties.

Framework accommodates various machine learning models.

Abstract

In recent years, precision treatment strategy have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating conditional average treatment effects (CATE) in time-to-event data with competing risks, using ICU patients with sepsis as an illustrative example. Our approach, based on cumulative incidence functions and targeted maximum likelihood estimation (TMLE), achieves both asymptotic efficiency and double robustness. The primary contribution of this work lies in our derivation of the efficient influence function for the targeted causal parameter, CATE. We established the theoretical proofs for these properties, and subsequently confirmed them through simulations. Our TMLE framework is flexible, accommodating various regression and machine learning models, making it applicable in diverse scenarios. In order to identify variables contributing to treatment effect heterogeneity and to facilitate accurate estimation of CATE, we developed two distinct variable importance measures (VIMs). This work provides a powerful tool for optimizing personalized treatment strategies, furthering the pursuit of precision medicine.