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Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.
- Source :
- Health Services & Outcomes Research Methodology; Dec2016, Vol. 16 Issue 4, p213-233, 21p
- Publication Year :
- 2016
-
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 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13873741
- Volume :
- 16
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Health Services & Outcomes Research Methodology
- Publication Type :
- Academic Journal
- Accession number :
- 119282367
- Full Text :
- https://doi.org/10.1007/s10742-016-0159-3