Work

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

Year: 2016

Type: article

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.

Source: Health Services and Outcomes Research Methodology

DOI: 10.1007/s10742-016-0159-3

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

Institutions: Johns Hopkins University, Sidney Kimmel Comprehensive Cancer Center

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,590