title: Incorporating direct and indirect evidence using bayesian methods: an applied case study in ovarian cancer.
abstract: OBJECTIVE
  To demonstrate the application of a Bayesian mixed treatment comparison (MTC) model to synthesize data from clinical trials to inform decisions based on all relevant evidence.
  
  
  METHODS
  The value of an MTC model is demonstrated using a probabilistic decision-analytic model developed to assess the cost-effectiveness of second-line chemotherapy in ovarian cancer. Three clinical trials were found that each made a different pair-wise comparison of three treatments of interest in the overall patient population. As no common comparator existed between the three trials, an MTC model was used to assess the combined weight of evidence on survival from all three trials simultaneously. This analysis was compared to an alternative approach that combined two of the trials to make the same comparison of all three treatments using a common comparator, and an informal approach that did not synthesize the available evidence.
  
  
  RESULTS
  By including all three trials using an MTC model, the credible intervals around estimated overall survival were reduced compared with making the same comparison using only two trials and a common comparator. Nevertheless, the survival estimates from the MTC model result in greater uncertainty around the optimal treatment strategy at a cost-effectiveness threshold of 30,000 pounds per quality-adjusted life-year.
  
  
  CONCLUSIONS
  MTC models can be used to combine more data than would typically be included in a traditional meta-analysis that relies on a common comparator. They can formally quantify the combined uncertainty from all available evidence, and can be conducted using the same analytical approaches as standard meta-analyses.
authors.affiliation: Centre for Health Economics, University of York, York, UK. scg3@york.ac.uk
authors.forename: Susan
authors.initials: S
authors.lastname: Griffin
authors.forename: Laura
authors.initials: L
authors.lastname: Bojke
authors.forename: Caroline
authors.initials: C
authors.lastname: Main
authors.forename: Stephen
authors.initials: S
authors.lastname: Palmer
mesh_terms: D000970:Antineoplastic Agents / Q000191:economics / Q000627:therapeutic use; D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D015201:Meta-Analysis as Topic; D017059:Models, Econometric; D010051:Ovarian Neoplasms / Q000188:drug therapy / Q000401:mortality; D019057:Quality-Adjusted Life Years; D015996:Survival Rate; D013673:Technology Assessment, Biomedical; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents