Model of Complications of NIDDM: I. Model construction and assumptions

Eastman, Richard C.; Javitt, Jonathan C.; Herman, William H.; Dasbach, Erik J.; Zbrozek, Arthur S.; Dong, Fred B.; Manninen, Diane L.; Garfield, Sanford A.; Copley-Merriman, Catherine; Maier, William C.; Eastman, Jeffery F.; Kotsanos, James G.; Cowie, Catherine C.; Harris, Maureen I.

doi:10.2337/diacare.20.5.725

Cited by 225 publications

(247 citation statements)

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“…They can also be used to estimate future healthcare costs of patients with Type 2 diabetes, but their main purpose is to estimate the cost-effectiveness of different disease management strategies, especially when evidence of the impact of interventions on surrogate endpoints is limited, or where evidence from clinical trials has to be extrapolated over patients' lifetimes. Currently, there are at least five simulation models being used in these ways [1,2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the first model of the progression of Type 2 diabetes had separate modules for cardiovascular disease, retinopathy, nephropathy and neuropathy [1], and used a probabilistic Monte-Carlo analysis to simulate event histories over the remaining lifetimes of newly diagnosed patients with Type 2 diabetes. While that model represented a landmark in the use of computer simulation to model the progression of the disease, it had several limitations.…”

Section: Introductionmentioning

confidence: 99%

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A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68)

et al. 2004

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Aims/hypothesis. The aim of this study was to develop a simulation model for Type 2 diabetes that can be used to estimate the likely occurrence of major diabetes-related complications over a lifetime, in order to calculate health economic outcomes such as qualityadjusted life expectancy. Methods. Equations for forecasting the occurrence of seven diabetes-related complications and death were estimated using data on 3642 patients from the United Kingdom Prospective Diabetes Study (UKPDS). After examining the internal validity, the UKPDS Outcomes Model was used to simulate the mean difference in expected quality-adjusted life years between the UKPDS regimens of intensive and conventional blood glucose control. Results. The model's forecasts fell within the 95% confidence interval for the occurrence of observed events during the UKPDS follow-up period. When the model was used to simulate event history over patients' lifetimes, those treated with a regimen of conventional glucose control could expect 16.35 undiscounted quality-adjusted life years, and those receiving treatment with intensive glucose control could expect 16.62 quality-adjusted life years, a difference of 0.27 (95% CI: −0.48 to 1.03). Conclusions/interpretations. The UKPDS OutcomesModel is able to simulate event histories that closely match observed outcomes in the UKPDS and that can be extrapolated over patients' lifetimes. Its validity in estimating outcomes in other groups of patients, however, remains to be evaluated. The model allows simulation of a range of long-term outcomes, which should assist in informing future economic evaluations of interventions in Type 2 diabetes. The centres of the UKPDS are listed at the end of the paper Conflict of interest. Several authors (as indicated above) are employed by the University of Oxford. This paper describes and places in the public domain a simulation model that we have called the UKPDS Outcomes Model. All of the information necessary to reproduce the UKPDS Outcomes Model is provided in this article, but it is conceivable that a future user with a commercial interest in the UKPDS Outcomes Model might prefer to use the software already created by University programmers. Depending on the nature of the proposed use of the UKPDS Outcomes Model, the University of Oxford might charge a fee in this case.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68)

et al. 2004

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“…To be useful for informing strategic commissioning, we required a transparent, simple [45,52]), but some like the Archimedes, the CORE or the EAGLE model are designed for both type 1 and 2 [53][54][55]. We tested the adequacy of our model through validation, sensitivity analysis and comparing results with those from more sophisticated models.…”

Section: Modelling Requirements Of Our Frameworkmentioning

confidence: 99%

Requisite models for strategic commissioning: the example of type 1 diabetes

Airoldi¹,

Bevan²,

Morton³

et al. 2008

Health Care Manage Sci

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A developing emphasis of health care reforms has been creating organisations with responsibilities for strategic commissioning of services for defined populations. Such organisations must set priorities in aiming to meet their populations' needs subject to a budget constraint. This requires estimates of the health benefits and costs of different interventions for their populations. This paper outlines a framework that does this and shows how this requires modelling to produce estimates in a way that is transparent to commissioners, of requisite complexity to produce sound estimates for priority setting using routinely available data. The example illustrated in this paper is an intervention that would improve glucose control in the English population with type 1 diabetes. It takes many years for a change in glucose management to deliver maximum benefits; hence the intervention is not good value-for-money in the short run. We aim to give a more strategic view of the costs and benefits modelling costs and benefits in a steady-state model which suggests that the intervention is good value-for-money in the long run.

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“…[131][132][133] These models used similar assumptions to a previously published model by Eastman and colleagues, 134,135 but incorporated a screening module to assess the impact of identifying and treating patients earlier than they otherwise would have been. The Center for Disease Control Diabetes Cost-Effectiveness Study Group (CDC) was the first to construct a model assessing the impact of screening.…”

Section: Statement Of the Decision Problemmentioning

confidence: 99%

Screening for type 2 diabetes: literature review and economic modelling

2007

Clinical Governance: An International Journal

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Model of Complications of NIDDM: I. Model construction and assumptions

Cited by 225 publications

References 0 publications

A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68)

A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68)

Requisite models for strategic commissioning: the example of type 1 diabetes

Screening for type 2 diabetes: literature review and economic modelling

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