Literature DB >> 26861793

Identification of Evidence for Key Parameters in Decision-Analytic Models of Cost Effectiveness: A Description of Sources and a Recommended Minimum Search Requirement.

Suzy Paisley1.   

Abstract

This paper proposes recommendations for a minimum level of searching for data for key parameters in decision-analytic models of cost effectiveness and describes sources of evidence relevant to each parameter type. Key parameters are defined as treatment effects, adverse effects, costs, resource use, health state utility values (HSUVs) and baseline risk of events. The recommended minimum requirement for treatment effects is comprehensive searching according to available methodological guidance. For other parameter types, the minimum is the searching of one bibliographic database plus, where appropriate, specialist sources and non-research-based and non-standard format sources. The recommendations draw on the search methods literature and on existing analyses of how evidence is used to support decision-analytic models. They take account of the range of research and non-research-based sources of evidence used in cost-effectiveness models and of the need for efficient searching. Consideration is given to what constitutes best evidence for the different parameter types in terms of design and scientific quality and to making transparent the judgments that underpin the selection of evidence from the options available. Methodological issues are discussed, including the differences between decision-analytic models of cost effectiveness and systematic reviews when searching and selecting evidence and comprehensive versus sufficient searching. Areas are highlighted where further methodological research is required.

Mesh:

Year:  2016        PMID: 26861793     DOI: 10.1007/s40273-015-0372-x

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  16 in total

Review 1.  Literature searching for clinical and cost-effectiveness studies used in health technology assessment reports carried out for the National Institute for Clinical Excellence appraisal system.

Authors:  P Royle; N Waugh
Journal:  Health Technol Assess       Date:  2003       Impact factor: 4.014

Review 2.  Review of guidelines for good practice in decision-analytic modelling in health technology assessment.

Authors:  Z Philips; L Ginnelly; M Sculpher; K Claxton; S Golder; R Riemsma; N Woolacoot; J Glanville
Journal:  Health Technol Assess       Date:  2004-09       Impact factor: 4.014

Review 3.  Sources of information on adverse effects: a systematic review.

Authors:  Su Golder; Yoon K Loke
Journal:  Health Info Libr J       Date:  2010-09

4.  Classification of evidence in decision-analytic models of cost-effectiveness: a content analysis of published reports.

Authors:  Suzy Paisley
Journal:  Int J Technol Assess Health Care       Date:  2010-10-06       Impact factor: 2.188

Review 5.  How much searching is enough? Comprehensive versus optimal retrieval for technology assessments.

Authors:  Andrew Booth
Journal:  Int J Technol Assess Health Care       Date:  2010-10-06       Impact factor: 2.188

Review 6.  Developing efficient search strategies to identify reports of adverse effects in MEDLINE and EMBASE.

Authors:  Su Golder; Heather M McIntosh; Steve Duffy; Julie Glanville
Journal:  Health Info Libr J       Date:  2006-03

7.  Assessing searches in NICE single technology appraisals: practice and checklist.

Authors:  Ruth Wong; Suzy Paisley; Christopher Carroll
Journal:  Int J Technol Assess Health Care       Date:  2013-06-17       Impact factor: 2.188

Review 8.  Appropriate evidence sources for populating decision analytic models within health technology assessment (HTA): a systematic review of HTA manuals and health economic guidelines.

Authors:  Ingrid Zechmeister-Koss; Petra Schnell-Inderst; Günther Zauner
Journal:  Med Decis Making       Date:  2013-10-17       Impact factor: 2.583

Review 9.  Ezetimibe for the treatment of hypercholesterolaemia: a systematic review and economic evaluation.

Authors:  R Ara; I Tumur; A Pandor; A Duenas; R Williams; A Wilkinson; S Paisley; J Chilcott
Journal:  Health Technol Assess       Date:  2008-05       Impact factor: 4.014

Review 10.  The contribution of different information sources for adverse effects data.

Authors:  Su Golder; Yoon K Loke
Journal:  Int J Technol Assess Health Care       Date:  2012-04       Impact factor: 2.188

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  3 in total

Review 1.  Structural Design and Data Requirements for Simulation Modelling in HIV/AIDS: A Narrative Review.

Authors:  Xiao Zang; Emanuel Krebs; Linwei Wang; Brandon D L Marshall; Reuben Granich; Bruce R Schackman; Julio S G Montaner; Bohdan Nosyk
Journal:  Pharmacoeconomics       Date:  2019-10       Impact factor: 4.981

2.  GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making.

Authors:  Jan L Brozek; Carlos Canelo-Aybar; Elie A Akl; James M Bowen; John Bucher; Weihsueh A Chiu; Mark Cronin; Benjamin Djulbegovic; Maicon Falavigna; Gordon H Guyatt; Ami A Gordon; Michele Hilton Boon; Raymond C W Hutubessy; Manuela A Joore; Vittal Katikireddi; Judy LaKind; Miranda Langendam; Veena Manja; Kristen Magnuson; Alexander G Mathioudakis; Joerg Meerpohl; Dominik Mertz; Roman Mezencev; Rebecca Morgan; Gian Paolo Morgano; Reem Mustafa; Martin O'Flaherty; Grace Patlewicz; John J Riva; Margarita Posso; Andrew Rooney; Paul M Schlosser; Lisa Schwartz; Ian Shemilt; Jean-Eric Tarride; Kristina A Thayer; Katya Tsaioun; Luke Vale; John Wambaugh; Jessica Wignall; Ashley Williams; Feng Xie; Yuan Zhang; Holger J Schünemann
Journal:  J Clin Epidemiol       Date:  2020-09-24       Impact factor: 6.437

3.  Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis.

Authors:  Emanuel Krebs; Benjamin Enns; Linwei Wang; Xiao Zang; Dimitra Panagiotoglou; Carlos Del Rio; Julia Dombrowski; Daniel J Feaster; Matthew Golden; Reuben Granich; Brandon Marshall; Shruti H Mehta; Lisa Metsch; Bruce R Schackman; Steffanie A Strathdee; Bohdan Nosyk
Journal:  PLoS One       Date:  2019-05-30       Impact factor: 3.240

  3 in total

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