Precision medicine: Subgroup identification in longitudinal trajectories

Yishu Wei*, Lei Liu, Xiaogang Su, Lihui Zhao, Hongmei Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment’s overall performance. In this study, we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories while subgroup identification requires a further evaluation of differential treatment effects among subgroups induced by moderators. To this end, we propose a tree-structured subgroup identification method, termed “interaction tree for longitudinal trajectories”, which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial is presented.

Original languageEnglish (US)
Pages (from-to)2603-2616
Number of pages14
JournalStatistical Methods in Medical Research
Volume29
Issue number9
DOIs
StatePublished - Sep 1 2020

Keywords

  • Recursive partitioning
  • interaction tree
  • personalized medicine
  • precision medicine

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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