Network Meta-Analysis of Categorical Outcomes with Incomplete Data

• Activity Number: 515
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Christopher Schmid*+ and Thomas A Trikalinos and Ingram Olkin
• Companies: Brown University and Brown University and Stanford University
• Keywords: correlated outcomes ; Markov chain Monte Carlo ; missing data ; multinomial distribution ; multiple treatments meta-analysis

Abstract

We develop a Bayesian multinomial network meta-analysis model for unordered (nominal) categorical outcomes that allows for partially observed data in which exact event counts may not be known for each category. This model properly accounts for correlations of counts in mutually exclusive categories and enables proper comparison and ranking of treatment effects across multiple treatments and multiple outcome categories. We apply the model to analyze 17 trials, each of which compares two of three treatments (high and low dose statins and standard care/control) for some combination of the six outcomes of fatal and non-fatal stroke, fatal and non-fatal myocardial infarction, other causes of mortality, or no event.