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

care pathway comparator diagnostic economic model diagnostic test evaluation standard of care

Journal

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
ISSN: 1524-4733
Titre abrégé: Value Health
Pays: United States
ID NLM: 100883818

Informations de publication

Date de publication:
22 Feb 2024
Historique:
received: 09 10 2023
revised: 08 02 2024
accepted: 14 02 2024
medline: 25 2 2024
pubmed: 25 2 2024
entrez: 24 2 2024
Statut: aheadofprint

Résumé

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 two pragmatic literature reviews and nine 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, modellers 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 (e.g. 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 modellers when identifying and operationalising the SoC comparator in diagnostic cost-effectiveness models.

Identifiants

pubmed: 38401794
pii: S1098-3015(24)00072-X
doi: 10.1016/j.jval.2024.02.003
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc. All rights reserved.

Auteurs

Sara Graziadio (S)

Review and Evidence Synthesis, York Health Economics Consortium, Enterprise House, University of York, Innovation Way, York, YO10 5NQ, UK. Electronic address: sara.graziadio@york.ac.uk.

Emily Gregg (E)

Review and Evidence Synthesis, York Health Economics Consortium, Enterprise House, University of York, Innovation Way, York, YO10 5NQ, UK.

A Joy Allen (AJ)

Health Economics, Roche Diagnostics UK and Ireland, Charles Avenue, Burgess Hill, RH15 9RY, UK.

Paul Neveux (P)

Global Access & Policy, Roche Diagnostics International AG, Forrenstrasse 2, 6343 Rotkreuz, Switzerland.

Brigitta Monz (B)

Global Access & Policy, Roche Diagnostics International AG, Forrenstrasse 2, 6343 Rotkreuz, Switzerland.

Clare Davenport (C)

Institute of Applied Health Research, University of Birmingham, Birmingham, UK.

Stuart Mealing (S)

Health Economics and Outcomes Research, York Health Economics Consortium, Enterprise House, University of York, Innovation Way, York, YO10 5NQ, UK.

Hayden Holmes (H)

Digital Health Technology, York Health Economics Consortium, Enterprise House, University of York, Innovation Way, York, YO10 5NQ, UK.

Lavinia Ferrante di Ruffano (L)

Review and Evidence Synthesis, York Health Economics Consortium, Enterprise House, University of York, Innovation Way, York, YO10 5NQ, UK.

Classifications MeSH