TY - JOUR
T1 - Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research
AU - Henderson, Nicholas C.
AU - Louis, Thomas A.
AU - Wang, Chenguang
AU - Varadhan, Ravi
N1 - Funding Information:
This work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-1303-5896). We would also like to thank the following members of the PCORI expert/advisory panel for their insightful discussions: David Banks, Scott Berry, Bradley Carlin, Steve Goodman, Paul Gustafson, Frank Harrell, J. Jack Lee, Roderick Little, David Ohlssen, Gene Pennello, Gary Rosner, and Tyler VanderWeele.
Publisher Copyright:
© 2016, The Author(s).
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
KW - Bayesian subgroup analysis
KW - Heterogeneity of treatment effect
KW - Hierarchical modeling
KW - Personalized medicine
KW - Precision medicine
KW - Treatment–covariate interaction
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U2 - 10.1007/s10742-016-0159-3
DO - 10.1007/s10742-016-0159-3
M3 - Article
C2 - 27881932
AN - SCOPUS:84988667281
SN - 1387-3741
VL - 16
SP - 213
EP - 233
JO - Health Services and Outcomes Research Methodology
JF - Health Services and Outcomes Research Methodology
IS - 4
ER -