Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, A. Deusinglaan1, 9713AV, Groningen, The Netherlands.
Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
Acta Diabetol. 2023 Jul;60(7):861-879. doi: 10.1007/s00592-023-02045-8. Epub 2023 Mar 3.
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.
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.
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.
The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.
糖尿病健康经济学 (HE) 模型在决策中发挥着重要作用。对于大多数 2 型糖尿病 (T2D) 的 HE 模型，核心模型涉及并发症预测。然而，HE 模型的综述很少关注预测模型的纳入。本综述的目的是调查预测模型如何被纳入 T2D 的 HE 模型，并确定挑战和可能的解决方案。
从 1997 年 1 月 1 日至 2022 年 11 月 15 日，检索 PubMed、Web of Science、Embase 和 Cochrane，以确定发表的 T2D HE 模型。手动搜索参与 Mount Hood Diabetes Simulation Modeling Database 或之前挑战的所有模型。由两名独立作者进行数据提取。研究了 HE 模型的特征、其潜在的预测模型以及纳入预测模型的方法。
该范围综述确定了 34 个 HE 模型，包括一个连续时间面向对象模型 (n=1)、离散时间状态转移模型 (n=18) 和离散时间离散事件模拟模型 (n=15)。发表的预测模型通常用于模拟并发症风险，例如 UKPDS (n=20)、Framingham (n=7)、BRAVO (n=2)、NDR (n=2) 和 RECODe (n=2)。确定了四种方法将不同并发症的相互依存预测模型结合起来，包括随机顺序评估 (n=12)、同时评估 (n=4)、“向日葵方法” (n=3) 和预定义顺序 (n=1)。其余研究未考虑相互依存关系或报告不明确。
在 HE 模型中整合预测模型的方法需要进一步关注，特别是关于如何选择、调整和排序预测模型。
