Phil McEwan1, Volker Foos2, James L Palmer2, Mark Lamotte3, Adam Lloyd4, David Grant4. 1. Centre for Health Economics, Swansea University, Wales, UK; Health Economics and Outcomes Research Ltd., Monmouth, UK. Electronic address: phil.mcewan@heor.co.uk. 2. Health Economics and Outcomes Research, IMS Health, Basel, Switzerland. 3. Health Economics and Outcomes Research, IMS Health, Brussels, Belgium. 4. Health Economics and Outcomes Research, IMS Health, London, UK.
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 (R(2)) goodness-of-fit measure. RESULTS: Across all validation studies, the CDM simulations produced an R(2) statistic of 0.90. For validation studies with a follow-up duration of less than 15 years, R(2) 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 R(2) of 0.72. In T2DM, validating against 20-year outcomes data (UKPDS) resulted in an R(2) 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.
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 diabetespatients, 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 (R(2)) goodness-of-fit measure. RESULTS: Across all validation studies, the CDM simulations produced an R(2) statistic of 0.90. For validation studies with a follow-up duration of less than 15 years, R(2) 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 R(2) of 0.72. In T2DM, validating against 20-year outcomes data (UKPDS) resulted in an R(2) 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.
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