Published Apr 2014
Berger M, Martin B, Husereau D, et al. A questionnaire to assess the relevance and credibility of observational studies to inform healthcare decision making: an ISPOR-AMCP-NPC Good Practice Task Force Report. Value Health. 2014;17(2):143-156.
Evidence-based health care decisions are best informed by comparisons
of all relevant interventions used to treat conditions in specific patient
populations. Observational studies are being performed to help fill
evidence gaps. Widespread adoption of evidence from observational
studies, however, has been limited because of various factors, including
the lack of consensus regarding accepted principles for their evaluation
and interpretation. Two task forces were formed to develop questionnaires
to assist decision makers in evaluating observational studies, with
one Task Force addressing retrospective research and the other Task
Force addressing prospective research.
The intent was to promote a structured approach to reduce the potential for subjective interpretation of evidence and drive consistency in decision making. Separately developed questionnaires were combined into a single questionnaire consisting of 33 items. These were divided into two domains: relevance and credibility. Relevance addresses the extent to which findings, if accurate, apply to the setting of interest to the decision maker. Credibility addresses the extent to which the study findings accurately answer the study question. The questionnaire provides a guide for assessing the degree of confidence that should be placed from observational studies and promotes awareness of the subtleties involved in evaluating those.
Keywords: bias, checklist, comparative effectiveness research, confounding, consensus, credibility, decision making, prospective observational study, quality, questionnaire, relevance, retrospective observational study, validity.
Copyright © 2017, 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