Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 1


Jansen, JP; Fleurence, R; Devine, B; Itzler, R; Barrett, A; Hawkins, N; Lee, K; Boersma, C; Annemans, L; Cappelleri, JC; (2011) Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 1. Value in health, 14 (4). pp. 417-28. ISSN 1098-3015 DOI: https://doi.org/10.1016/j.jval.2011.04.002

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Abstract

: Evidence-based health-care decision making requires comparisons of all relevant competing interventions. In the absence of randomized, controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best choice(s) of treatment. Mixed treatment comparisons, a special case of network meta-analysis, combine direct and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than a traditional meta-analysis. This report from the ISPOR Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on the interpretation of indirect treatment comparisons and network meta-analysis to assist policymakers and health-care professionals in using its findings for decision making. We start with an overview of how networks of randomized, controlled trials allow multiple treatment comparisons of competing interventions. Next, an introduction to the synthesis of the available evidence with a focus on terminology, assumptions, validity, and statistical methods is provided, followed by advice on critically reviewing and interpreting an indirect treatment comparison or network meta-analysis to inform decision making. We finish with a discussion of what to do if there are no direct or indirect treatment comparisons of randomized, controlled trials possible and a health-care decision still needs to be made.<br/>

Item Type: Article
Keywords: Advisory Committees, Advisory Committees: standards, Bayesian, Comparative effectiveness, Data Interpretation, Decision Making, Decision making, Delivery of Health Care, Delivery of Health Care: standards, Delivery of Health Care: statistics & numerical da, Economics, Humans, Indirect treatment comparison, Meta-Analysis as Topic, Mixed treatment comparison, Network meta-analysis, Outcome Assessment (Health Care), Outcome Assessment (Health Care): standards, Outcome Assessment (Health Care): statistics & num, Pharmaceutical, Pharmaceutical: standards, Pharmaceutical: statistics & numerical, Randomized Controlled Trials as Topic, Randomized Controlled Trials as Topic: methods, Research Design, Research Design: standards, Research Report, Research Report: standards, Statistical, Treatment Outcome, bayesian, comparative effectiveness, decision making, indi-, mixed treatment comparison, network, rect treatment comparison, Advisory Committees, standards, Data Interpretation, Statistical, Decision Making, Delivery of Health Care, standards, statistics & numerical data, Economics, Pharmaceutical, standards, statistics & numerical data, Humans, Meta-Analysis as Topic, Outcome Assessment (Health Care), standards, statistics & numerical data, Randomized Controlled Trials as Topic, methods, Research Design, standards, Research Report, standards, Treatment Outcome
Faculty and Department: Faculty of Public Health and Policy > Dept of Health Services Research and Policy
Research Centre: Centre for Statistical Methodology
PubMed ID: 21669366
Web of Science ID: 299080800004
URI: http://researchonline.lshtm.ac.uk/id/eprint/2534849

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