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Objectives: Given the lack of a value framework for assessing health technologies in Asian settings, a value framework incorporating multiple-criteria decision analysis for new drugs under universal healthcare coverage in Taiwan was established., Methods: The development process included (1) the adoption of 5 value domains (ie, Overall clinical benefit, Disease burden, Alignment with patient concerns, Economic value, and Feasibility of adoption into the health system) and 26 corresponding indicators, derived from the literature and expert discussions; (2) the creation of separate weighting schemes for 3 drug types-new oncology, new orphan, and other new drugs-based on inputs from multiple stakeholders (n = 86) using various weighting methods; and (3) the application of the value framework to cases of new oncology drugs., Results: Overall clinical benefit had the highest preference weight, irrespective of drug type, (ie, mean values [95% CIs] for new oncology, new orphan, and other new drugs: 32.5 [30.4-34.6], 30.6 [28.1-33.1], and 30.6 [28.7-32.6], respectively), weighting method, and stakeholder type. The 5 domain-derived weights (from the point allocation method) were comparable to the 26 indicator-derived weights (from the direct rating method), suggesting that the value framework with a short-form (domain-derived) weighting scheme is sufficient to support decision making under time and resource constraints., Conclusions: A country-specific value framework incorporating multiple-criteria decision analysis for new drugs was developed in an Asian setting under universal healthcare coverage. It allows multiple stakeholders to systematically appraise all drug value attributes and provides a structured process for adapting and refining value assessments., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section. Dr Hsiao is an editor for Value in Health and had no role in the peer-review process of this article., (Copyright © 2025. Published by Elsevier Inc.)

Background: Most existing risk equations for predicting/stratifying individual diabetic kidney disease (DKD) risks were developed using relatively dated data from selective and homogeneous trial populations comprising predominately Caucasian type 2 diabetes (T2D) patients. We seek to adapt risk equations for prediction of DKD progression (microalbuminuria, macroalbuminuria, and renal failure) using empiric data from a real-world population with T2D in Taiwan., Methods: Risk equations from three well-known simulation models: UKPDS-OM2, RECODe, and CHIME models, were adapted. Discrimination and calibration were determined using the area under the receiver operating characteristic curve (AUROC), a calibration plot (slope and intercept), and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was performed for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of risk equations to address risk variations among patients., Results: The RECODe equations for microalbuminuria and macroalbuminuria showed moderate discrimination (AUROC: 0.62 and 0.76) but underestimated the event risks (calibration slope > 1). The CHIME equation had the best discrimination for renal failure (AUROCs from CHIME, UKPDS-OM2 and RECODe: 0.77, 0.60 and 0.64, respectively). All three equations overestimated renal failure risk (calibration slope < 1). After rigorous updating, the calibration slope/intercept of the recalibrated RECODe for predicting microalbuminuria (0.87/0.0459) and macroalbuminuria (1.10/0.0004) risks as well as the recalibrated CHIME equation for predicting renal failure risk (0.95/-0.0014) were improved., Conclusions: Risk equations for prediction of DKD progression in real-world Taiwanese T2D patients were established, which can be incorporated into a multi-state simulation model to project and differentiate individual DKD risks for supporting timely interventions and health economic research., (© 2024. The Author(s).)