Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification

J Biopharm Stat. 2019;29(6):1082-1102. doi: 10.1080/10543406.2019.1584204. Epub 2019 Mar 12.

Abstract

Subgroup analysis, as the key component of personalized medicine development, has attracted a lot of interest in recent years. While a number of exploratory subgroup searching approaches have been proposed, informative evaluation criteria and scenario-based systematic comparison of these methods are still underdeveloped topics. In this article, we propose two evaluation criteria in connection with traditional type I error and power concepts, and another criterion to directly assess recovery performance of the underlying treatment effect structure. Extensive simulation studies are carried out to investigate empirical performance of a variety of tree-based exploratory subgroup methods under the proposed criteria. A real data application is also included to illustrate the necessity and importance of method evaluation.

Keywords: GUIDE; T-AIC/T-BIC; interaction tree; qualitative interaction trees; virtual twins.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Area Under Curve
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Precision Medicine*