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

TitleLook before you leap: systematic evaluation of tree-based statistical methods in subgroup identification.
Publication TypeJournal Article
Year of Publication2019
AuthorsLiu, Yang, Xiwen Ma, Donghui Zhang, Lijiang Geng, Xiaojing Wang, Wei Zheng, and Ming-Hui Chen
JournalJ Biopharm Stat
Volume29
Issue6
Pagination1082-1102
Date Published2019
ISSN1520-5711
KeywordsArea Under Curve, Computer Simulation, Data Interpretation, Statistical, Precision Medicine
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.

DOI10.1080/10543406.2019.1584204
Alternate JournalJ Biopharm Stat
Original PublicationLook before you leap: Systematic evaluation of tree-based statistical methods in subgroup identification.
PubMed ID30859903
PubMed Central IDPMC6742587
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States