Some methods for heterogeneous treatment effect estimation in high-dimensions
This work addresses the challenge of leveraging high-dimensional observational data to improve treatment decisions for patients, though it appears incremental in its methodological contributions.
The authors tackled the problem of making personalized treatment recommendations from high-dimensional observational electronic medical records by proposing three methods for estimating heterogeneous treatment effects. They reported that their methods performed well in simulations across various treatment effect functions and applied two promising methods to data from a large randomized trial on high blood pressure treatment.
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records (EMRs) that are only just recently being leveraged to inform better treatment recommendations. These data present a unique challenge because they are high-dimensional and observational. Our goal is to make personalized treatment recommendations based on the outcomes for past patients similar to a new patient. We propose and analyze three methods for estimating heterogeneous treatment effects using observational data. Our methods perform well in simulations using a wide variety of treatment effect functions, and we present results of applying the two most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.