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. 2014 Mar;17(2):157-73.
doi: 10.1016/j.jval.2014.01.004.

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

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Free article

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

Jeroen P Jansen et al. Value Health. 2014 Mar.
Free article

Erratum in

  • Value Health. 2016 Jan;19(1):121

Abstract

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.

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