Hierarchical Bayesian Methods for Combining Efficacy and Safety in Multiple Treatment Comparisons
View Presentation *Bradley P. Carlin, University of Minnesota Haitao Chu, University of Minnesota Hwanhee Hong, University of Minnesota Keywords: Bayesian methods, Indirect comparisons, Missing data, Biomedical decision makers confronted with questions about the comparative effectiveness and safety of interventions often wish to combine all sources of data. Such multiple treatment comparisons (MTCs) often rely largely on indirect comparisons. In such settings, hierarchical Bayesian meta-analytic methods offer a natural approach (e.g., by enabling full posterior inference on the probability that a given treatment is best). We summarize the current state of such methods for binary and continuous responses, and consider extension to multiple outcomes in a missing data framework. We also propose a new arm-based model that is less constrained than existing models. We illustrate our methods with data from two recent MTCs, one comparing pharmacological treatments for female urinary incontinence, and another on physical therapy interventions for knee pain secondary to osteoarthritis.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC