The importance of model structure in the cost-effectiveness analysis of primary care interventions for the management of hypertension

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • University of Leeds

Abstract

Background: Management of hypertension can lead to significant reductions in blood pressure, thereby reducing the risk of cardiovascular disease (CVD). Modelling the course of CVD is not without complications, and uncertainty surrounding the structure of a model will almost always arise once a choice of a model structure is defined.
Objective: To provide a practical illustration of the impact on the results of cost-effectiveness of changing or adapting model structures in a previously published cost utility analysis of a primary care intervention for the management of hypertension (TASMIN-SR).
Methods: Case study assessing structural uncertainty arising from model structure and from the exclusion of secondary events. Four alternative model structures were implemented. Long-term cost-effectiveness was estimated and the results compared to those from the TASMIN-SR model.
Results: The main cost-effectiveness results obtained in the TASMIN-SR study did not change with the implementation of alternative model structures. Choice of model type was limited to a cohort Markov model and, due to lack of epidemiological data, only Model 4 captured structural uncertainty arising from the exclusion of secondary events in the case study model.
Conclusion: The results of this study indicate that main conclusions drawn from the TASMIN-SR model of cost-effectiveness were robust to changes in model structure and the inclusion of secondary events. Even though one of the models produced results that were different to those of TASMIN-SR, the fact that the main conclusions were identical suggests that a more parsimonious model may have sufficed.

Details

Original languageEnglish
JournalValue in Health
Early online date19 Oct 2017
Publication statusE-pub ahead of print - 19 Oct 2017

Keywords

  • decision-analytic modelling, cardiovascular disease, hypertension, structural uncertainty, modelling