Title |
Use of Indirect and Mixed Treatment Comparisons for Technology Assessment
|
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Published in |
PharmacoEconomics, September 2012
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DOI | 10.2165/00019053-200826090-00006 |
Pubmed ID | |
Authors |
Alex Sutton, A. E. Ades, Nicola Cooper, Keith Abrams |
Abstract |
Indirect and mixed treatment comparison (MTC) approaches to synthesis are logical extensions of more established meta-analysis methods. They have great potential for estimating the comparative effectiveness of multiple treatments using an evidence base of trials that individually do not compare all treatment options. Connected networks of evidence can be synthesized simultaneously to provide estimates of the comparative effectiveness of all included treatments and a ranking of their effectiveness with associated probability statements. The potential of the use of indirect and MTC methods in technology assessment is considerable, and would allow for a more consistent assessment than simpler alternative approaches. Although such models can be viewed as a logical and coherent extension of standard pair-wise meta-analysis, their increased complexity raises some unique issues with far-reaching implications concerning how we use data in technology assessment, while simultaneously raising searching questions about standard pair-wise meta-analysis. This article reviews pair-wise meta-analysis and indirect and MTC approaches to synthesis, clearly outlining the assumptions involved in each approach. It also raises the issues that the National Institute for Health and Clinical Excellence (NICE) needed to consider in updating their 2004 Guide to the Methods of Technology Appraisal, if such methods are to be used in their technology appraisals. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 3% |
Brazil | 3 | 2% |
United States | 2 | 2% |
Spain | 1 | <1% |
Unknown | 113 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 33 | 27% |
Student > Ph. D. Student | 19 | 15% |
Student > Master | 12 | 10% |
Other | 11 | 9% |
Professor | 9 | 7% |
Other | 19 | 15% |
Unknown | 20 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 47 | 38% |
Economics, Econometrics and Finance | 11 | 9% |
Mathematics | 9 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 8 | 7% |
Computer Science | 4 | 3% |
Other | 14 | 11% |
Unknown | 30 | 24% |