Use of Decision Modelling in Economic Evaluations of Diagnostic Tests: An Appraisal and Review of Health Technology Assessments in the UK.
Yang Y., Abel L., Buchanan J., Fanshawe T., Shinkins B.
Diagnostic tests play an important role in the clinical decision-making process by providing information that enables patients to be identified and stratified to the most appropriate treatment and management strategies. Decision analytic modelling facilitates the synthesis of evidence from multiple sources to evaluate the cost effectiveness of diagnostic tests. This study critically reviews the methods used to model the cost effectiveness of diagnostic tests in UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) reports. UK NIHR HTA reports published between 2009 and 2018 were screened to identify those reporting an economic evaluation of a diagnostic test using decision analytic modelling. Existing decision modelling checklists were identified in the literature and a modified checklist tailored to diagnostic economic evaluations was developed, piloted and used to assess the diagnostic models in HTA reports. Of 728 HTA reports published during the study period, 55 met the inclusion criteria. The majority of models performed well with a clearly defined decision problem and analytical perspective (89% of HTAs met the criterion). The model structure usually reflected the care pathway and progression of the health condition. However, there are areas requiring improvement. These are predominantly systematic identification of treatment effects (20% met), poor selection of comparators (50% met) and assumed independence of tests used in sequence (32% took correlation between sequential tests into consideration). The complexity and constraints of performing decision analysis of diagnostic tests on costs and health outcomes makes it particularly challenging and, as a result, quality issues remain. This review provides a comprehensive assessment of modelling in HTA reports, highlights problems and gives recommendations for future diagnostic modelling practice.