Use of Decision Models in the Development of Evidence-Based Clinical Preventive Services Recommendations: Methods of the U.S. Preventive Services Task Force.

Published

Journal Article

The U.S. Preventive Services Task Force (USPSTF) develops evidence-based recommendations about preventive care based on comprehensive systematic reviews of the best available evidence. Decision models provide a complementary, quantitative approach to support the USPSTF as it deliberates about the evidence and develops recommendations for clinical and policy use. This article describes the rationale for using modeling, an approach to selecting topics for modeling, and how modeling may inform recommendations about clinical preventive services. Decision modeling is useful when clinical questions remain about how to target an empirically established clinical preventive service at the individual or program level or when complex determinations of magnitude of net benefit, overall or among important subpopulations, are required. Before deciding whether to use decision modeling, the USPSTF assesses whether the benefits and harms of the preventive service have been established empirically, assesses whether there are key issues about applicability or implementation that modeling could address, and then defines the decision problem and key questions to address through modeling. Decision analyses conducted for the USPSTF are expected to follow best practices for modeling. For chosen topics, the USPSTF assesses the strengths and limitations of the systematically reviewed evidence and the modeling analyses and integrates the results of each to make preventive service recommendations.

Full Text

Cited Authors

  • Owens, DK; Whitlock, EP; Henderson, J; Pignone, MP; Krist, AH; Bibbins-Domingo, K; Curry, SJ; Davidson, KW; Ebell, M; Gillman, MW; Grossman, DC; Kemper, AR; Kurth, AE; Maciosek, M; Siu, AL; LeFevre, ML; U.S. Preventive Services Task Force*,

Published Date

  • October 2016

Published In

Volume / Issue

  • 165 / 7

Start / End Page

  • 501 - 508

PubMed ID

  • 27379742

Pubmed Central ID

  • 27379742

Electronic International Standard Serial Number (EISSN)

  • 1539-3704

International Standard Serial Number (ISSN)

  • 0003-4819

Digital Object Identifier (DOI)

  • 10.7326/m15-2531

Language

  • eng