Abstract:
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One important aspect of precision medicine is to allow physicians to choose the most suitable treatment for their patients, which requires an understanding of the heterogeneity of treatment effects from a patient-centric view. With large amount of genetic data being generated, a full picture of individuals' characteristics is forming. Recent development using machine learning methods within the counterfactual framework shows great potential in analyzing such data. In this research, we develop a meta-learner approach to estimate individual treatment effect (ITE) for survival outcome. We consider two algorithms, T-learner and X-learner, each combined with three machine learning methods: random survival forest, Bayesian accelerate failure time model, and deep survival neural network. The performance of these methods is compared through simulations. We then apply the methods on a randomized clinical trial (RCT), the AREDS study for age-related macular degeneration, to estimate ITEs and identify genetic variants that contribute to the heterogeneous treatment effects. The resulting treatment recommendation rules are applied to a subsequent RCT, AREDS2, as an external validation.
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