OBJECTIVES: To review the sources and quality of evidence used in the development of economic decision models in health technology assessments (HTAs). METHODS: All economic decision models developed as part of the NHS Research and Development HTA Programme between 1997 and 2003 were reviewed. Quality of evidence was assessed using a hierarchy of data sources developed for economic analyses. RESULTS: Decision models are parameterized using diverse sources of evidence (e.g. randomized controlled trials, observational studies, expert opinion). Evidence on the main clinical effect was mostly identified and quality assessed as part of the companion systematic review/meta-analysis of the HTA and therefore reported in a transparent and reproducible way. For the other model inputs (i.e. adverse events, baseline clinical data, resource use and utilities), the search strategies for identifying relevant evidence were rarely made explicit and in a number of reports the sources of specific evidence were unclear due to poor reporting. CONCLUSIONS: A more formal and replicable approach to identification and assessment of quality of model inputs is required to reduce the 'black box' nature of decision models, and lead to less scepticism regarding model outputs.
OBJECTIVES: To review the sources and quality of evidence used in the development of economic decision models in health technology assessments (HTAs). METHODS: All economic decision models developed as part of the NHS Research and Development HTA Programme between 1997 and 2003 were reviewed. Quality of evidence was assessed using a hierarchy of data sources developed for economic analyses. RESULTS: Decision models are parameterized using diverse sources of evidence (e.g. randomized controlled trials, observational studies, expert opinion). Evidence on the main clinical effect was mostly identified and quality assessed as part of the companion systematic review/meta-analysis of the HTA and therefore reported in a transparent and reproducible way. For the other model inputs (i.e. adverse events, baseline clinical data, resource use and utilities), the search strategies for identifying relevant evidence were rarely made explicit and in a number of reports the sources of specific evidence were unclear due to poor reporting. CONCLUSIONS: A more formal and replicable approach to identification and assessment of quality of model inputs is required to reduce the 'black box' nature of decision models, and lead to less scepticism regarding model outputs.
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