Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.
Pubmed ID: 27881932
Pubmed Central ID: PMC5097788
Journal: Health services & outcomes research methodology
Publication Date: Jan. 1, 2016
Authors: Wang C, Varadhan R, Henderson NC, Louis TA
Cite As: Henderson NC, Louis TA, Wang C, Varadhan R. Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research. Health Serv Outcomes Res Methodol 2016;16(4):213-233. Epub 2016 Sep 20.
Studies:
Abstract
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 <b>beanz</b> 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.