Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data

Stat Med. 2024 Sep 30;43(22):4388-4436. doi: 10.1002/sim.10167. Epub 2024 Jul 25.

Abstract

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.

Keywords: individualized treatment regimen; personalized medicine; subgroup identification.

Publication types

  • Review

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical
  • Observational Studies as Topic* / methods
  • Observational Studies as Topic* / statistics & numerical data
  • Randomized Controlled Trials as Topic* / methods
  • Randomized Controlled Trials as Topic* / statistics & numerical data
  • Treatment Effect Heterogeneity
  • Treatment Outcome