A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes
Pub. online: 5 February 2024
Type: Statistical Data Science
Open Access
Open Access
†
Contributed equally.
Received
13 October 2023
13 October 2023
Accepted
17 January 2024
17 January 2024
Published
5 February 2024
5 February 2024
Abstract
One crucial aspect of precision medicine is to allow physicians to recommend the most suitable treatment for their patients. This requires understanding the treatment heterogeneity from a patient-centric view, quantified by estimating the individualized treatment effect (ITE). With a large amount of genetics data and medical factors being collected, a complete picture of individuals’ characteristics is forming, which provides more opportunities to accurately estimate ITE. Recent development using machine learning methods within the counterfactual outcome framework shows excellent potential in analyzing such data. In this research, we propose to extend meta-learning approaches to estimate individualized treatment effects with survival outcomes. Two meta-learning algorithms are considered, T-learner and X-learner, each combined with three types of machine learning methods: random survival forest, Bayesian accelerated failure time model and survival neural network. We examine the performance of the proposed methods and provide practical guidelines for their application in randomized clinical trials (RCTs). Moreover, we propose to use the Boruta algorithm to identify risk factors that contribute to treatment heterogeneity based on ITE estimates. The finite sample performances of these methods are compared through extensive simulations under different randomization designs. The proposed approach is applied to a large RCT of eye disease, namely, age-related macular degeneration (AMD), to estimate the ITE on delaying time-to-AMD progression and to make individualized treatment recommendations.
Supplementary material
Supplementary Material
1.
We provide codes and data in GitHub https://github.com/nab1779321/HTEsurv that can be used to reproduce the simulation and real data analysis in this paper.
2.
We provide an additional pdf file that includes a) additional simulation results and b) additional real data analysis results.
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