DOI: 10.6084/m9.figshare.29301340
title: Validation of the IHE type 2 diabetes cohort model in the Japanese clinical setting

description: Economic simulation models, such as the IHE Type 2 Diabetes Cohort Model (IHE-DCM-T2), are used widely to inform resource allocation for Type 2 Diabetes (T2D) treatments. Recently, IHE-DCM-T2 was augmented with Japanese-specific risk equations to align with the Japanese healthcare context. This study extends prior model validation of IHE-DCM-T2 to cover the Japanese risk equations for applications in Japan’s clinical setting and healthcare system. Face validity was assessed through expert review of model assumptions and structure. Model programming was verified by code review and 728 stress tests. Predictive accuracy was tested by comparing model predictions to real-world outcomes from 28 Japanese studies, assessing concordance visually, with regression lines, and with mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), mean squared logarithmic error (MSLE), and mean squared log-accuracy ratio (MSLAR). Subgroup analyses examined dependent and independent endpoints, along with mortality, microvascular, and macrovascular outcomes. Sensitivity analyses assessed robustness to variations in scale and sample size. IHE-DCM-T2 demonstrated face validity and correct implementation. External validation against 120 endpoints showed good alignment between predicted and observed events, with regression line slope=0.96 and R<sup>2</sup>=0.98. Overall, prediction errors were: MAPE=0.83, RMSPE=1.21, MSLE=0.61, and MSLAR=0.53. Predictions were more accurate for dependent than independent endpoints. Among endpoint categories, macrovascular events had the lowest average errors, whereas mortality endpoints had the highest MAPE and RMSPE, and microvascular endpoints had highest MSLE and MSLAR. Predictive accuracy was consistent across alternative test specifications. Limitations included gaps in validation data, and the requirement for long-term follow-up that inherently reflects past treatment patterns. Only studies with at least 1,000 patients were included, which may introduce selection bias. This comprehensive validation of the IHE-DCM-T2, augmented with Japanese-specific risk equations, demonstrated its suitability for health technology assessments and resource allocation decisions for T2D in the Japanese clinical setting and healthcare system. Type 2 diabetes (T2D) is a growing health concern in Japan, with approximately 15 million people living with diabetes, placing significant pressure on the healthcare system. To help decision-makers understand which treatments offer the best value for money, researchers use simulation models that predict long-term effects and costs of choosing different options. One such model, the IHE Type 2 Diabetes Cohort Model (IHE-DCM-T2), was recently updated with data specific to Japanese patients to better reflect local health outcomes and clinical practices. In this study, we tested how well the model performs when predicting real-world outcomes for Japanese people with T2D. We compared the model’s predictions to results from 28 Japanese clinical studies and found that it accurately estimated the chances of developing diabetes-related complications like heart disease and kidney problems. Not surprisingly, the model closely matched outcomes from the Japanese data it was built on, but it also performed well when tested against other Japanese studies. A Japanese clinical expert also reviewed the model to confirm that it provides a realistic and scientifically accurate representation of diabetes care and disease progression in Japan. Although the model had to use some international data due to gaps in Japanese sources, it was found to provide an overall realistic picture of diabetes in Japan. These findings suggest that the updated IHE-DCM-T2 model is a reliable tool for evaluating diabetes treatments in Japan and may help healthcare policymakers and clinicians make better-informed decisions that improve patient outcomes and ensure efficient use of healthcare resources.