An alternative parameterization of Bayesian logistic hierarchical models for mixed treatment comparisons

Pharm Stat. 2015 Jul-Aug;14(4):322-31. doi: 10.1002/pst.1688. Epub 2015 May 7.


Mixed treatment comparison (MTC) models rely on estimates of relative effectiveness from randomized clinical trials so as to respect randomization across treatment arms. This approach could potentially be simplified by an alternative parameterization of the way effectiveness is modeled. We introduce a treatment-based parameterization of the MTC model that estimates outcomes on both the study and treatment levels. We compare the proposed model to the commonly used MTC models using a simulation study as well as three randomized clinical trial datasets from published systematic reviews comparing (i) treatments on bleeding after cirrhosis, (ii) the impact of antihypertensive drugs in diabetes mellitus, and (iii) smoking cessation strategies. The simulation results suggest similar or sometimes better performance of the treatment-based MTC model. Moreover, from the real data analyses, little differences were observed on the inference extracted from both models. Overall, our proposed MTC approach performed as good, or better, than the commonly applied indirect and MTC models and is simpler, fast, and easier to implement in standard statistical software.

Keywords: Bayesian inference; meta-analysis; mixed treatments comparison.

Publication types

  • Comparative Study

MeSH terms

  • Antihypertensive Agents / therapeutic use
  • Bayes Theorem
  • Computer Simulation
  • Data Interpretation, Statistical
  • Gastrointestinal Hemorrhage / etiology
  • Gastrointestinal Hemorrhage / therapy
  • Humans
  • Liver Cirrhosis / complications
  • Logistic Models
  • Meta-Analysis as Topic*
  • Odds Ratio
  • Randomized Controlled Trials as Topic / methods
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Research Design / statistics & numerical data*
  • Smoking Cessation
  • Treatment Outcome


  • Antihypertensive Agents