Is the Comparator in Your Diagnostic Cost-Effectiveness Model “Standard of Care”? Recommendations from Literature Reviews and Expert Interviews on How to Identify and Operationalize It

• Published in: Value in health Vol. 27; no. 5; pp. 585 - 597
• Main Authors: Graziadio, Sara, Gregg, Emily, Allen, A. Joy, Neveux, Paul, Monz, Brigitta U., Davenport, Clare, Mealing, Stuart, Holmes, Hayden, Ferrante di Ruffano, Lavinia
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-05-2024
• Subjects: care pathway; comparator; Cost-Benefit Analysis; diagnostic economic model; diagnostic test evaluation; Humans; Interviews as Topic; Models, Economic; Standard of Care; comparator; standard of care; diagnostic economic model; care pathway; diagnostic test evaluation
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2024.02.003

This research aimed to develop best-practice recommendations for identifying the “standard of care” (SoC) and integrate it when it is the comparator in diagnostic economic models (SoC comparator).
A multi-methods approach comprising 2 pragmatic literature reviews and 9 expert interviews was used. Experts rated their agreement with draft recommendations based on the authors’ analysis of the reviews. These were refined iteratively to produce final recommendations.
Fourteen best-practice recommendations are provided. Care pathway mapping (using quantitative, qualitative, or mixed-methods approaches) should be used for identifying the SoC comparator. Guidelines analysis can be integrated with expert opinion to identify pathway variability and discrepancies from clinical practice. For integrating the SoC comparator into the model, recommendations around structure, input sourcing, data aggregation and reporting, input uncertainty, and model variability are presented. For example, modelers should consider that the reference standard is not synonymous with the SoC, and the SoC may not be the only comparator. The comparator limitations should be discussed with clinical experts, but elicitation of its diagnostic accuracy is not recommended. Probabilistic sensitivity analysis is recommended when evaluating the overall input uncertainty, and deterministic sensitivity analysis is useful when there is high model uncertainty or SoC variability. Consensus could not be reached for some topics (eg, the role of real-world data, model averaging, and alternative model structures), but the reported discussions provide points for consideration.
To our knowledge, this is the first guidance to support modelers when identifying and operationalizing the SoC comparator in diagnostic cost-effectiveness models.
•Modeling the cost-effectiveness requires a comparison of the new diagnostic and patient management strategy with a comparator, often a complex standard of care (SoC) pathway. The new diagnostic test can replace or, more commonly, be integrated with the diagnostic SoC pathway. This presents 2 main challenges in obtaining useful estimates of cost-effectiveness: (1) identifying the diagnostic SoC pathway to define the comparator and (2) integrating it into the model.•We identified possible methodological approaches through pragmatic literature reviews and expert interviews. Some examples are to identify the SoC comparator via mapping the care pathway through qualitative, quantitative, or mixed-methods approaches; to select data on the diagnostic accuracy of the SoC comparator from studies in which the reference standard is the one used for the new test and report detailed information of the data source; and to assess the impact of the SoC pathway variability through scenario analysis.•We provide 14 recommendations that can support modelers to minimize the risk of a misrepresentation of the incremental clinical utility of the new diagnostic test because of the uncertainty and variability of the SoC comparator. We identified practice recommendations, gaps in knowledge, and areas where further research would advance the field.