• Mathematics, Medicine
  • Published in Research synthesis methods 2014
  • DOI:10.1002/jrsm.1103

Bayesian network meta-analysis for unordered categorical outcomes with incomplete data.

@article{Schmid2014BayesianNM,
  title={Bayesian network meta-analysis for unordered categorical outcomes with incomplete data.},
  author={Christopher H. Schmid and Thomas A. Trikalinos and Ingram Olkin},
  journal={Research synthesis methods},
  year={2014},
  volume={5 2},
  pages={
          162-85
        }
}
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… CONTINUE READING