Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Nicholas C. Henderson, Thomas A. Louis, Chenguang Wang, Ravi Varadhan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.

Original languageEnglish (US)
Pages (from-to)213-233
Number of pages21
JournalHealth Services and Outcomes Research Methodology
Issue number4
StatePublished - Dec 1 2016


  • Bayesian subgroup analysis
  • Heterogeneity of treatment effect
  • Hierarchical modeling
  • Personalized medicine
  • Precision medicine
  • Treatment–covariate interaction

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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