A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus
Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM.
This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM.
A systematic review of Embase®, MEDLINE®, MEDLINE® In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities.
We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis.
Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of ‘best in class’ methods for the different technical aspects of T1DM modelling.
KeywordsHealth Technology Assessment Probabilistic Sensitivity Analysis Risk Equation Macrovascular Event Primary Publication
Martin Henriksson and Ramandeep Jindal were employees of PAREXEL International when the study was conducted, which received funding from AstraZeneca to conduct the systematic literature review and manuscript preparation. Michael Willis is employed by the Swedish Institute for Health Economics, which received funding from AstraZeneca for consulting services, including design of the systematic literature review and manuscript preparation. Catarina Sternhufvud, Klas Bergenheim, and Elisabeth Sörstadius are employees of AstraZeneca; AstraZeneca provided financial funding of the study design and conduct and manuscript preparation. All authors were involved in the development of the systematic literature review and have reviewed and approved the final content of this manuscript.
Editorial support was provided by Elizabeth Griffiths and Sean Walsh at PAREXEL International and was funded by AstraZeneca.
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