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

arXiv: https://arxiv.org/abs/2407.18389 · HTML: https://mlsanity.com/paper/2407.18389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings](https://mlsanity.com/paper/2606.18281/) (2026)

## Abstract (arXiv)

In recent years, precision treatment strategies have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating subgroup average treatment effects (ATE) 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, subgroup ATE. 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 subgroup ATE, 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.