Prediction of complications in health economic models of type 2 diabetes: a review of methods used

Journal article

Aim

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

Methods

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

Results

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

Conclusions

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Authors: Li, X; Li, F; Wang, J; van Giessen, A; Feenstra, TL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Acta Diabetologica
• Volume: 60
• Issue: 7
• Pages: 861-879
• Publication date: 2023-03-03
• DOI: 10.1007/s00592-023-02045-8
• EISSN: 1432-5233
• ISSN: 0940-5429
• Language: English
• Keywords: Predictive modelling; Diabetes mellitus; Computer science; Medicine; Machine learning; Type 2 diabetes