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TAKING PATIENT HETEROGENEITY AND PREFERENCES INTO ACCOUNT IN HEALTH TECHNOLOGY ASSESSMENTS.
Kievit, Wietske; Tummers, Marcia; van Hoorn, Ralph; Booth, Andrew; Mozygemba, Kati; Refolo, Pietro; Sacchini, Dario; Pfadenhauer, Lisa; Gerhardus, Ansgar; Van der Wilt, Gert Jan.
Affiliation
  • Kievit W; Radboud Institute for Health Scienceswietske.kievit@radboudumc.nl.
  • Tummers M; Radboud Institute for Health Sciences.
  • van Hoorn R; Radboud Institute for Health Sciences.
  • Booth A; Health Economics and Decision Science (HEDS).
  • Mozygemba K; Institute for Public Health and Nursing Research (IPP).
  • Refolo P; Institute of Bioethics and Medical Humanities.
  • Sacchini D; Institute of Bioethics and Medical Humanities.
  • Pfadenhauer L; Institute for Biometrics,Epidemiology and Medical Informatics.
  • Gerhardus A; Institute for Public Health and Nursing Research (IPP).
  • Van der Wilt GJ; Donders Institute for Brain,Cognition and Behaviour.
Int J Technol Assess Health Care ; 33(5): 562-569, 2017 Jan.
Article in En | MEDLINE | ID: mdl-29065947
OBJECTIVES: The INTEGRATE-HTA project provided methodology to evaluate complex technologies. This study provides guidance on how to retrieve and critically appraise available evidence on moderators and predictors of treatment effects and on patient preferences for treatment outcomes as a source of complexity. METHODS: Search filters for PubMed were developed by hand-searching a large volume of articles reporting on relevant aspects. Search terms were retrieved from selected papers and algorithmically combined to find the optimal combination of search terms. For the development of the appraisal checklists literature was searched in PubMed and Google Scholar together with citation chasing. For the CHecklist for the Appraisal of Moderators and Predictors (CHAMP) a Delphi procedure was used to value a set of eligible appraisal criteria retrieved from the literature. RESULTS: Search filters were developed optimized for different accuracy measures. The final version of CHAMP consists of a seventeen questions covering the design, analysis, results and transferability of results of moderator and predictor analysis. The final checklist for appraisal of literature on patient preferences for treatment outcomes consist of six questions meant to help the user to identify relevant quality issues together with a guidance toward existing tools concerning the appraisal of specific preference elicitation methods. CONCLUSIONS: Incorporating knowledge on subgroups for whom a specific treatment will produce more benefit holds the promise of better targeting and, ultimately, enhancing overall effectiveness and efficiency of healthcare technology. Finally, incorporating information on preferences for treatment outcomes will foster health technology assessment that addresses outcomes that are important to patients.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Research Design / Technology Assessment, Biomedical / Patient Preference Type of study: Guideline / Health_technology_assessment / Prognostic_studies Limits: Humans Language: En Journal: Int J Technol Assess Health Care Journal subject: PESQUISA EM SERVICOS DE SAUDE Year: 2017 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Research Design / Technology Assessment, Biomedical / Patient Preference Type of study: Guideline / Health_technology_assessment / Prognostic_studies Limits: Humans Language: En Journal: Int J Technol Assess Health Care Journal subject: PESQUISA EM SERVICOS DE SAUDE Year: 2017 Type: Article