Random forests of interaction trees for estimating individualized treatment effects in randomized trials

Xiaogang Su, Annette T. Peña, Lei Liu, Richard A. Levine

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the “separate regression” approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.

Original languageEnglish
Pages (from-to)2547-2560
Number of pages14
JournalStatistics in medicine
Volume37
Issue number17
DOIs
StatePublished - Jul 30 2018

Keywords

  • individualized treatment effects
  • infinitesimal jackknife
  • precision medicine
  • random forests
  • treatment-by-covariate interaction

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