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Some methods for heterogeneous treatment effect estimation in high dimensions
Author(s) -
Powers Scott,
Qian Junyang,
Jung Kenneth,
Schuler Alejandro,
Shah Nigam H.,
Hastie Trevor,
Tibshirani Robert
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7623
Subject(s) - observational study , computer science , variety (cybernetics) , randomized controlled trial , estimation , sprint , data science , medical physics , medicine , intensive care medicine , artificial intelligence , surgery , management , software engineering , economics
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 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 3 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 2 most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.