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.
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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Authors: Wiebke Mohr; Anika Rädke; Adel Afi; Franka Mühlichen; Moritz Platen; Annelie Scharf; Bernhard Michalowsky; Wolfgang Hoffmann Journal: Int J Environ Res Public Health Date: 2022-07-13 Impact factor: 4.614
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