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

Petros Pechlivanoglou, Fentaw Abegaz, Maarten J Postma, Ernst Wit

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)322-331
Number of pages10
JournalPharmaceutical Statistics
Volume14
Issue number4
DOIs
Publication statusPublished - 7-May-2015

Keywords

  • Bayesian inference
  • Meta-analysis
  • Mixed treatments comparison
  • arm
  • bleeding
  • clinical trial
  • clinical trial (topic)
  • computer program
  • data analysis
  • diabetes mellitus
  • human
  • liver cirrhosis
  • meta analysis
  • model
  • randomization
  • simulation
  • smoking cessation
  • systematic review (topic)
  • antihypertensive agent

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