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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Schmitz S,Adams R,Walsh Cdoi
10.1002/sim.5764subject
Has Abstractpub_date
2013-07-30 00:00:00pages
2935-49issue
17eissn
0277-6715issn
1097-0258journal_volume
32pub_type
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