Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities.

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Abstract

The use of data from randomized clinical trials to justify treatment decisions for real-world patients is the current state of the art. It relies on the assumption that average treatment effects from the trial can be extrapolated to patients with personal and/or disease characteristics different from those treated in the trial. Yet, because of heterogeneity of treatment effects between patients and between the trial population and real-world patients, this assumption may not be correct for many patients. Using machine learning to estimate the expected conditional average treatment effect (CATE) in individual patients from observational data offers the potential for more accurate estimation of the expected treatment effects in each patient based on their observed characteristics. In this review, we discuss some of the challenges and opportunities for machine learning to estimate CATE, including ensuring identification assumptions are met, managing covariate shift, and learning without access to the true label of interest. We also discuss the potential applications as well as future work and collaborations needed to further improve identification and utilization of CATE estimates to increase patient benefit.

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Publication status: Published

Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, Clinical Research, Prevention, Clinical Trials and Supportive Activities, 3 Good Health and Well Being
Journal Title
Clin Pharmacol Ther
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Journal ISSN
0009-9236
1532-6535
Volume Title
Publisher
Wiley