Composite endpoints in health technology assessment: part 1 - an illustration of best modeling practice

Briggs, Andrew, Angelis, Aris ORCID: 0000-0002-0261-4634, Chen, Jieling, Booth, David, Davis, Jason A., Vaduganathan, Muthiah and Jhund, Pardeep S. (2025) Composite endpoints in health technology assessment: part 1 - an illustration of best modeling practice. Journal of Comparative Effectiveness Research, 14 (10). ISSN 2042-6305

Text (briggs-et-al-2025-composite-endpoints-in-health-technology-assessment-part-1-an-illustration-of-best-modeling-practice (1)) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB)

• https://www.webofscience.com/api/gateway...

Abstract

Composite endpoints amalgamate multiple clinical outcomes into a single measure, offering efficiency gains in clinical trials through increased event rates and reduced sample sizes, thus accelerating clinical development and regulatory approval. However, employing composite endpoints introduces complexities into health technology assessments (HTAs), particularly in economic modeling, due to the varying clinical significance and cost implications of the components. In this paper, we explore best modeling practice for HTAs that are based on clinical trials that employ composite endpoints. We examine regulatory guidance and discuss statistical solutions for differential component impacts, before presenting a case study based on a recent dapagliflozin submission for reimbursement in heart failure. Our investigation reveals that while composite endpoints can streamline trial analyses and hasten regulatory approval, they also pose a risk of bias in HTA if treatment effects for the components are inappropriately pooled. The paper discusses HTA principles in the context of composite endpoint trials and proposes strategies to develop modeling scenarios and interpret results, especially concerning whether to combine or split out estimates of component treatment effects. A particular focus is the accurate capture of uncertainty, both in terms of the parameter inputs to the model and over the ultimate decision to reimburse. This paper serves as a potential resource for researchers, practitioners and decision-makers, offering insights into best modeling practices that can unlock the full potential of composite endpoints in the pursuit of evidence-based healthcare decision-making.

Item Type:	Article
Additional Information:	© 2025 The authors
Divisions:	Health Policy LSE Health
Subjects:	R Medicine > RA Public aspects of medicine
Date Deposited:	23 Sep 2025 08:30
Last Modified:	20 Oct 2025 18:21
URI:	http://eprints.lse.ac.uk/id/eprint/129564

Actions (login required)

View Item