Published Jun 2014
Jansen JP, Trikalinos T, Cappelleri JC, et al. Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: An ISPOR-AMCP-NPC Good Practice Task Force Report. Value Health. 2014;17(2):157-173.
Despite the great realized or potential value of network meta-analysis
of randomized controlled trial evidence to inform health care decision
making, many decision makers might not be familiar with these
techniques. The Task Force developed a consensus-based 26-item
questionnaire to help decision makers assess the relevance and
credibility of indirect treatment comparisons and network meta-analysis
to help inform health care decision making. The relevance
domain of the questionnaire (4 questions) calls for assessments about
the applicability of network meta-analysis results to the setting of
interest to the decision maker. The remaining 22 questions belong to
an overall credibility domain and pertain to assessments about
whether the network meta-analysis results provide a valid answer
to the question they are designed to answer by examining 1) the used
evidence base, 2) analysis methods, 3) reporting quality and
transparency, 4) interpretation of findings, and 5) conflicts of interest.
The questionnaire aims to help readers of network meta-analysis opine about their confidence in the credibility and applicability of the results of a network meta-analysis, and help make decision makers aware of the subtleties involved in the analysis of networks of randomized trial evidence. It is anticipated that user feedback will permit periodic evaluation and modification of the questionnaire.
Keywords: bias, checklist, credibility, decision making, indirect comparisons, mixed treatment comparisons, multiple treatment comparison, network meta-analysis, questionnaire, relevance, validity.
Copyright © 2014, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.
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Tools for Health Care Decision Making: Observational Studies, Modeling Studies, and Network Meta-Analyses