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Med Decis Making. 2019 Oct;39(7):857-866. doi: 10.1177/0272989X19866415. Epub 2019 Sep 26.

Early Economic Evaluation of Diagnostic Technologies: Experiences of the NIHR Diagnostic Evidence Co-operatives.

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Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxfordshire, UK.
NIHR Diagnostic Evidence Co-operative Leeds, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Test Evaluation Group, Academic Unit of Health Economics, University of Leeds, Leeds, UK.
NIHR Diagnostic Evidence Co-operative London, Imperial College London, London, UK.
NIHR Diagnostic Evidence Co-operative Newcastle, Newcastle University, Newcastle upon Tyne, UK.
Newcastle upon Tyne Hospitals Foundation Trust, Newcastle upon Tyne, UK.
Health Economics Group, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK.
Cancer Research UK Edinburgh Centre, University of Edinburgh, Leeds, West Yorkshire, UK.


Diagnostic tests are expensive and time-consuming to develop. Early economic evaluation using decision modeling can reduce commercial risk by providing early evidence on cost-effectiveness. The National Institute for Health Research Diagnostic Evidence Co-operatives (DECs) was established to catalyze evidence generation for diagnostic tests by collaborating with commercial developers; DEC researchers have consequently made extensive use of early modeling. The aim of this article is to summarize the experiences of the DECs using early modeling for diagnostics. We draw on 8 case studies to illustrate the methods, highlight methodological strengths and weaknesses particular to diagnostics, and provide advice. The case studies covered diagnosis, screening, and treatment stratification. Treatment effectiveness was a crucial determinant of cost-effectiveness in all cases, but robust evidence to inform this parameter was sparse. This risked limiting the usability of the results, although characterization of this uncertainty in turn highlighted the value of further evidence generation. Researchers evaluating early models must be aware of the importance of treatment effect evidence when reviewing the cost-effectiveness of diagnostics. Researchers planning to develop an early model of a test should also 1) consult widely with clinicians to ensure the model reflects real-world patient care; 2) develop comprehensive models that can be updated as the technology develops, rather than taking a "quick and dirty" approach that may risk producing misleading results; and 3) use flexible methods of reviewing evidence and evaluating model results, to fit the needs of multiple decision makers. Decision models can provide vital information for developers at an early stage, although limited evidence mean researchers should proceed with caution.


cohort analysis; decision-analytic modeling; diagnostic test; early modeling; health economic evaluation

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