Emulate Randomized Clinical Trials Using Heterogeneous Treatment Effect Estimation for Personalized Treatments: Methodology Review and Benchmark

Authors

Yaobin Ling
Pulakesh Upadhyaya
Luyao Chen
Xiaoqian Jiang
Yejin Kim

Publication Date

1-1-2023

Journal

Journal of Biomedical Informatics

Abstract

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been limited translational research into the real-world healthcare domain. To fill the gap, we reviewed and compared eleven recent HTE estimation methodologies, including meta-learner, representation learning models, and tree-based models. We performed a comprehensive benchmark experiment based on nationwide healthcare claim data with application to Alzheimer's disease drug repurposing. We provided some challenges and opportunities in HTE estimation analysis in the healthcare domain to close the gap between innovative HTE models and deployment to real-world healthcare problems.

Keywords

Humans, Benchmarking, Randomized Controlled Trials as Topic, Causality, Machine Learning

Comments

Supplementary Materials

PMID: 36455806