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Value Health. 2011 Mar-Apr;14(2):371-80. doi: 10.1016/j.jval.2010.09.001. Epub 2011 Feb 5.

How valuable are multiple treatment comparison methods in evidence-based health-care evaluation?

Author information

1
Department of Health Sciences, University of Leicester, University Road, Leicester, UK. njc21@le.ac.uk

Abstract

OBJECTIVES:

To compare the use of pair-wise meta-analysis methods to multiple treatment comparison (MTC) methods for evidence-based health-care evaluation to estimate the effectiveness and cost-effectiveness of alternative health-care interventions based on the available evidence.

METHODS:

Pair-wise meta-analysis and more complex evidence syntheses, incorporating an MTC component, are applied to three examples: 1) clinical effectiveness of interventions for preventing strokes in people with atrial fibrillation; 2) clinical and cost-effectiveness of using drug-eluting stents in percutaneous coronary intervention in patients with coronary artery disease; and 3) clinical and cost-effectiveness of using neuraminidase inhibitors in the treatment of influenza. We compare the two synthesis approaches with respect to the assumptions made, empirical estimates produced, and conclusions drawn.

RESULTS:

The difference between point estimates of effectiveness produced by the pair-wise and MTC approaches was generally unpredictable-sometimes agreeing closely whereas in other instances differing considerably. In all three examples, the MTC approach allowed the inclusion of randomized controlled trial evidence ignored in the pair-wise meta-analysis approach. This generally increased the precision of the effectiveness estimates from the MTC model.

CONCLUSIONS:

The MTC approach to synthesis allows the evidence base on clinical effectiveness to be treated as a coherent whole, include more data, and sometimes relax the assumptions made in the pair-wise approaches. However, MTC models are necessarily more complex than those developed for pair-wise meta-analysis and thus could be seen as less transparent. Therefore, it is important that model details and the assumptions made are carefully reported alongside the results.

PMID:
21296599
DOI:
10.1016/j.jval.2010.09.001
[Indexed for MEDLINE]
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