Beanz: An R package for Bayesian analysis of heterogeneous treatment effects with a graphical user interface

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In patient-centered outcomes research, it is essential to assess the heterogeneity of treatment effects (HTE) when making health care decisions for an individual patient or a group of patients. Nevertheless, it remains challenging to evaluate HTE based on information collected from clinical studies that are often designed and conducted to evaluate the efficacy of a treatment for the overall population. The Bayesian framework offers a principled and flexible approach to estimate and compare treatment effects across subgroups of patients defined by their characteristics. In this paper, we describe the package beanz which facilitates the conduct of Bayesian analysis of HTE by allowing users to explore a wide range of Bayesian HTE analysis models and produce posterior inferences about HTE. The package beanz also provides a web-based graphical user interface (GUI) for users to conduct the Bayesian analysis of HTE in an interactive and user-friendly manner. With the GUI feature, package beanz can also be used by analysts not familiar with the R environment. We demonstrate package beanz using data from a randomized controlled trial on angiotensin converting enzyme inhibitor for treating congestive heart failure (N = 2569).

Original languageEnglish (US)
JournalJournal of Statistical Software
StatePublished - 2018


  • Bayesian analysis
  • GUI
  • HTE
  • Patient-centered outcomes research
  • R
  • Shiny
  • Stan
  • Subgroup analysis
  • Web-based Bayesian analysis

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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