title: Incorporating data from various trial designs into a mixed treatment comparison model.
abstract: Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates when head-to-head evidence is not available or insufficient. In recent years, this methodology has become widely accepted and applied in economic modelling of healthcare interventions. Most evaluations only consider evidence from randomized controlled trials, while information from other trial designs is ignored. In this paper, we propose three alternative methods of combining data from different trial designs in a mixed treatment comparison model. Naive pooling is the simplest approach and does not differentiate between-trial designs. Utilizing observational data as prior information allows adjusting for bias due to trial design. The most flexible technique is a three-level hierarchical model. Such a model allows for bias adjustment while also accounting for heterogeneity between-trial designs. These techniques are illustrated using an application in rheumatoid arthritis.
authors: Trinity College Dublin, Dublin, Ireland. schmitzs@tcd.ie
authors: Susanne
authors: S
authors: Schmitz
authors: Roisin
authors: R
authors: Adams
authors: Cathal
authors: C
authors: Walsh
mesh_terms: D001172:Arthritis, Rheumatoid / Q000628:therapy; D001499:Bayes Theorem; D015982:Bias; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
keywords: hierarchical modelling; mixed treatment comparison; multiple treatments meta-analysis; network meta-analysis; observational data; rheumatoid arthritis