Bayesian data analysis in observational comparative effectiveness research: rationale and examples.
Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.
Janssen Scientific Affairs, LLC, 1125 Trenton-Harbourton Road, Titusville, NJ 08560, USA.
William H
WH
Olson
Concetta
C
Crivera
Yi-Wen
YW
Ma
Jessica
J
Panish
Lian
L
Mao
Scott M
SM
Lynch
D054058:Acute Coronary Syndrome / Q000191:economics / Q000628:therapy; D001499:Bayes Theorem; D057186:Comparative Effectiveness Research / Q000379:methods*; D003695:Delivery of Health Care / Q000706:statistics & numerical data; D006801:Humans; D064887:Observational Studies as Topic / Q000379:methods*; D063868:Patient Outcome Assessment; D018802:Patient-Centered Care; D011446:Prospective Studies; D012107:Research Design; D012189:Retrospective Studies