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Activity Number: 13
Type: Invited
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #317712 View Presentation
Title: Bayesian Nonparametric Models for Comparative Effectiveness
Author(s): Gary L. Rosner* and Chenguang Wang
Companies: Johns Hopkins Medical Institution and Johns Hopkins Medical Institution
Keywords:
Abstract:

Comparative effectiveness research evaluates evidence of the effectiveness, benefits, and harms of different treatment choices to inform healthcare decision-making. Evidence often comes from research studies, such as randomized clinical trials, designed observational studies, and registries. While RCTs generally provide the highest level of evidence for comparing treatments, these studies are often carried out under more exacting conditions and enroll more narrowly defined populations than exist in the setting in which these treatments will find use. We discuss an application of a Bayesian nonparametric model to infer the benefit of a treatment in one of two hospital settings. We explore the use of this flexible model to account for heterogeneities in the patients and to predict outcomes for future patients.


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