Hierarchical Bayesian Methods for Combining Efficacy and Safety in Multiple Treatment Comparisons

• Activity Number: 233
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Hwanhee Hong*+ and Haitao Chu and Bradley P Carlin
• Companies: University of Minnesota and University of Minnesota and University of Minnesota
• Keywords: Mixed treatment comparisons ; Multiple binary outcomes ; Bayesian hierarchical model ; Meta-analysis ; Urinary incontinence

Abstract

Biomedical decision makers confronted with questions about the comparative effectiveness and safety of interventions often wish to combine all sources of data. Such mixed treatment comparisons (MTCs) may or may not include head-to-head randomized controlled trials of the treatments of primary interest, instead relying largely on indirect comparisons (say, trials that separately compare each treatment to placebo). In such settings, hierarchical Bayes-MCMC meta-analytic methods offer a natural approach. In this paper, we summarize the current state of such methods in the single binary response setting, and consider extension to the case of multiple outcomes (say, on both efficacy and safety) where we account for correlation and missingness, and use clinically-informed weights to arrive at an overall decision regarding the best treatment. We offer several simulations to support the use of our methods over more standard approaches that ignore cross-endpoint correlation. We also illustrate our methods with data from a recent MTC comparing pharmacological treatments for female urinary incontinence.