Validation of the CORE Diabetes Model against epidemiological and clinical studies.
ABSTRACT The aim of this study was to assess the validity of the CORE Diabetes Model by comparing results from model simulations with observed outcomes from published epidemiological and clinical studies in type 1 and type 2 diabetes.
A total of 66 second- (internal) and third- (external) order validation analyses were performed across a range of complications and outcomes simulated by the CORE Diabetes Model (amputation, cataract, hypoglycaemia, ketoacidosis, macular oedema, myocardial infarction, nephropathy, neuropathy, retinopathy, stroke and mortality). Published studies were reproduced in the model by recreating cohorts in terms of demographics, baseline risk factors and complications, treatment patterns and patient management strategies, and simulating the progress of the cohort to an equivalent time horizon.
Correlation analysis on results from 66 validation simulations produced an R2 value of 0.9224 (perfect fit = 1). A correlation plot of published study data versus values simulated by the CORE Diabetes Model had a trend line with a gradient of 1.0187 (perfect fit = 1). Validation analyses in type 1 and type 2 diabetes were associated with R2 values of 0.9778 and 0.8861 respectively. Correlation of second-order validation analyses (model predictions versus observed outcomes in studies used to construct the model) produced an R2 value of 0.9574, and the value for third-order analyses (model predictions versus observed outcomes in studies not used to construct the model) was 0.9023.
The CORE Diabetes Model provides an accurate representation of patient outcomes when compared to 66 studies of diabetes and its complications. Model flexibility ensures it can be used to compare diabetes management strategies in different cohorts across a variety of clinical settings.
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ABSTRACT: Background The IMS CORE Diabetes Model (CDM) is a widely published and validated simulation model applied in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) analyses. Validation to external studies is an important part of demonstrating model credibility. Objective Because the CDM is widely used to estimate long-term clinical outcomes in diabetes patients, the objective of this analysis was to validate the CDM to contemporary outcomes studies, including those with long-term follow-up periods. Methods A total of 112 validation simulations were performed, stratified by study follow-up duration. For long-term results (≥15-year follow-up), simulation cohorts representing baseline Diabetes Control and Complications Trial (DCCT) and United Kingdom Prospective Diabetes Study (UKPDS) cohorts were generated and intensive and conventional treatment arms were defined in the CDM. Predicted versus observed macrovascular and microvascular complications and all-cause mortality were assessed using the coefficient of determination (R2) goodness-of-fit measure. Results Across all validation studies, the CDM simulations produced an R2 statistic of 0.90. For validation studies with a follow-up duration of less than 15 years, R2 values of 0.90 and 0.88 were achieved for T1DM and T2DM respectively. In T1DM, validating against 30-year outcomes data (DCCT) resulted in an R2 of 0.72. In T2DM, validating against 20-year outcomes data (UKPDS) resulted in an R2 of 0.92. Conclusions This analysis supports the CDM as a credible tool for predicting the absolute number of clinical events in DCCT- and UKPDS-like populations. With increasing incidence of diabetes worldwide, the CDM is particularly important for health care decision makers, for whom the robust evaluation of health care policies is essential.Value in Health 09/2014; 17(6):714–724. DOI:10.1016/j.jval.2014.07.007 · 2.89 Impact Factor
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ABSTRACT: Promoting use of pharmaco-economic models by formulary reviewers is a goal of the Academy of Managed Care Pharmacy (AMCP) Format for Formulary Submissions, but relatively few decision makers use such models, and many doubt that they provide meaningful input. To demonstrate how sophisticated disease-based pharmaco-economic models can aid formulary decision makers when long-term outcomes data are lacking. The Center for Outcomes Research (CORE) Diabetes Model (CDM), a published, validated Markov pharmaco-economic model that projects clinical and economic endpoints, was used to model the cost-effectiveness of exenatide, a new injectable antidiabetic agent that enhances glucose-dependent insulin secretion, in a standard cohort of type 2 diabetes patients (mean body mass index [BMI] = 27.5 3 kg/m2), compared with a modified obese cohort (mean BMI = 35 3 kg/m2) that was otherwise demographically identical at baseline to the standard cohort. The standard cohort was assumed to maintain baseline weight during treatment, and the modified obese cohort was assumed to experience weight loss of approximately 9% (mean = 3 kg/m2), with corresponding improvements in blood pressure, low density lipoprotein cholesterol, and triglycerides. We selected a 30-year time horizon because it was the time interval during which the CDM predicted most of the subjects would have died, and the costs obtained thus reasonably projected lifetime total direct medical costs for these cohorts. While treatment options certainly will change over a 30-year period, our goal was to estimate the incremental effect of exenatide over other available therapies. The model predicted reduced long-term treatment costs in obese patients, driven by an 11% decrease in cardiovascular disease burden and derived from the presumed weight loss. The incremental cost-effectiveness ratio (ICER) for adding exenatide over 3 years was 35,000 dollars/quality-adjusted life-year (QALY). Using a 30-year horizon, ICER values were 13,000 dollars/QALY versus insulin, 32,000 dollars versus generic glyburide, and 16,000 dollars versus no additional treatment. Exenatide dominated pioglitazone. By comparison, the 30-year ICER for exenatide versus insulin in the nonobese cohort was 33,000 dollars. These results were presented to the pharmacy and therapeutics (P&T) committee and influenced its decision to add exenatide to the drug formulary. While our modeling assumed certain patient characteristics (e.g., obesity, need of further A1c reduction at baseline, motivation to lose weight), the P&T committee imposed only a step-therapy requirement to try either metformin or a sulfonylurea before trying exenatide and did adopt a nonspecific requirement for physician reauthorization of refills before the fourth pharmacy claim for exenatide. Disease-based pharmaco-economic models may help third party payers project costs and be particularly useful when only data from short-term clinical trials are available. In the present case, the pharmacy staff of a health plan used a pharmaco-economic model for drug treatment of type 2 diabetes provided by the manufacturer as part of the AMCP Format dossier process to project cost outcomes for exenatide, adjunct injectable therapy for patients taking metformin and/or sulfonylurea. The P&T committee approved the drug for inclusion in the drug formulary based in part on the results of the pharmaco-economic model produced from the cost inputs entered into the model by the health plan pharmacists.Journal of managed care pharmacy: JMCP 12(9):726-35. · 2.68 Impact Factor
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ABSTRACT: The research project 'Priority setting in chronic diseases: methodology for budget allocation' aims to develop a methodology to support optimal allocation of the health care budget with respect to chronic diseases. The current report describes the modelling steps required to address budget allocation questions regarding the prevention of chronic diseases and their complications with the RIVM Chronic Disease Model, with specific attention to diabetes mellitus. An extension of the RIVM Chronic Disease Model deals with the links between diabetes, its risk factors and its macrovascular complications. A health economics module computes outcomes in terms of intervention costs, costs of care and composite health effects. Finally, it is discussed how to formalize different preferences of policy makers in various objective functions and constraints. These three elements form the basis for the analysis of budget allocation questions in diabetes care. The model allows for the comparison of primary prevention with the prevention of complications in diagnosed patients as to costs of care and health effects. Furthermore, as it stands, the model with the health economics module per se is a useful tool for policy analysis, for instance, to compare the costs and effects of different interventions. Dit rapport beschrijft de elementen van een zogeheten 'budget allocatie model'. Dit model is bedoeld ter ondersteuning van beleidsmakers bij keuzes over de inzet van budget voor primaire preventie en/of preventie in de zorg bij chronische aandoeningen. Als concrete toepassing is gekozen voor Diabetes mellitus. Een uitbreiding van het RIVM Chronische Ziekten Model beschrijft het verband tussen diabetes, risicofactoren en hart- en vaatziektecomplicaties. Een gezondheidseconomische module berekent vervolgens gezondheidseffecten in termen van gewonnen levensjaren en voor kwaliteit van leven gecorrigeerde gewonnen levensjaren (QALYs), interventiekosten, en kosten van zorg. Ten slotte bespreken we hoe de voorkeuren van beleidsmakers kunnen worden geformaliseerd in doelstellingsfuncties en (budget-)beperkingen. Deze drie elementen zijn de basis voor een toepassing van budgetallocatie bij diabetes. De ontwikkelde methode is ook toepasbaar bij andere chronische ziekten, omdat we het bredere RIVM Chronische Ziekten model als uitgangspunt hebben gebruikt. Het nieuwe model voor diabetes is niet alleen een basis voor budgetallocatie, maar ook op zichzelf al bruikbaar om primaire preventie en verschillende vormen van preventie van complicaties bij diabetes te evalueren. Het model kan voor deze interventies de consequenties voor Nederland berekenen, zowel voor de kosten van zorg als voor de gezondheid.