Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups

Authors

Waverly Wei
Maya Petersen
Mark J van der Laan
Zeyu Zheng
Chong Wu
Jingshen Wang

Publication Date

9-1-2023

Journal

Biometrics

Abstract

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.

Keywords

Humans, Likelihood Functions, Computer Simulation, Propensity Score, Precision Medicine, Machine Learning

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Associated Data

PMID: 36416173