Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication.
The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. Methods: Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. Results: Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME-calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. Conclusions: The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed.
IntroductionCardiovascular disease is a leading cause of mortality in people with type 2 diabetes mellitus (T2DM). Beginning in 2015, long-term cardiovascular outcomes trials (CVOTs) have reported cardioprotective benefits for two classes of diabetes drugs. In addition to improving the lives of patients, these health benefits affect relative value (i.e., cost-effectiveness) of these agents compared with each other and especially compared with other agents. While long-term CVOT data on hard outcomes are a great asset, economic modeling of the value of this cardioprotection faces many new empirical challenges. The aim of this study was to identify different approaches used to incorporate drug-mediated cardioprotection into T2DM economic models, to identify pros and cons of these approaches, and to highlight additional considerations.MethodsA review of T2DM modeling applications (manuscript or conference abstracts) that included direct cardioprotective effects was conducted from January 2015 to September 2018. Model applications were classified on the basis of the mechanism used to model cardioprotection [i.e., directly via hazard ratios (HRs) for cardiovascular outcomes or indirectly via biomarker mediation]. Details were extracted and the studies were evaluated.ResultsFive full-length articles and 16 conference abstracts (of which 11 posters were found) qualified for study inclusion. While the approaches used were diverse, the five full-length publications and all but two of the abstracts modeled cardioprotection used direct HRs from the relevant CVOT. The remaining two posters modeled cardioprotection using CVOT HRs in combination with treatment effects mediated through known risk factors.ConclusionThe classification of empirical methods in cardioprotection was intended to facilitate a better understanding of the pros and cons of different methodologies. A substantial diversity was observed, though most used trial HRs directly. Given the differences observed, we believe that diabetes modelers and other stakeholders can benefit from a formal discussion and evolving consensus.FundingJanssen Global Services, LLC (Raritan, NJ, USA).Electronic supplementary materialThe online version of this article (10.1007/s13300-019-00681-4) contains supplementary material, which is available to authorized users.
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