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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Schmitz S,Adams R,Walsh C

doi

10.1002/sim.5764

subject

Has Abstract

pub_date

2013-07-30 00:00:00

pages

2935-49

issue

17

eissn

0277-6715

issn

1097-0258

journal_volume

32

pub_type

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