Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

INTRODUCTION

As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.

METHODS

This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.

RESULTS

The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.

CONCLUSION

Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.

Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction

Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.

How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease

Background. Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. Methods. We describe 4 challenges associated with modeling heterogeneity and structural, stochastic, and parameter uncertainty that can be encountered during the development of DES models. We explain why these are important and how to correctly implement them. To illustrate the impact of the modeling choices discussed, we use (results of) a model for chronic obstructive pulmonary disease (COPD) as a case study. Results. The results from the case study showed that, under a correct implementation of the uncertainty in the model, a hypothetical intervention can be deemed as cost-effective. The consequences of incorrect modeling uncertainty included an increase in the incremental cost-effectiveness ratio ranging from 50% to almost a factor of 14, an extended life expectancy of approximately 1.4 years, and an enormously increased uncertainty around the model outcomes. Thus, modeling uncertainty incorrectly can have substantial implications for decision making. Conclusions. This article provides guidance on the implementation of uncertainty in DES models and improves the transparency of reporting uncertainty methods. The COPD case study illustrates the issues described in the article and helps understanding them better. The model R code shows how the uncertainty was implemented. For readers not familiar with R, the model's pseudo-code can be used to understand how the model works. By doing this, we can help other developers, who are likely to face similar challenges to those described here.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design

BACKGROUND

Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

OBJECTIVE

The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

METHODS

The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models.

RESULTS

The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years).

DISCUSSION

This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.

Cost-effectiveness of Initiating an Insulin Pump in T1D Adults Using Continuous Glucose Monitoring Compared with Multiple Daily Insulin Injections: The DIAMOND Randomized Trial

BACKGROUND

The economic impact of both continuous glucose monitoring (CGM) and insulin pumps (continuous subcutaneous insulin infusion [CSII]) in type 1 diabetes (T1D) have been evaluated separately. However, the cost-effectiveness of adding CSII to existing CGM users has not yet been assessed.

OBJECTIVE

The aim of this study was to evaluate the societal cost-effectiveness of CSII versus continuing multiple daily injections (MDI) in adults with T1D already using CGM.

METHODS

In the second phase of the DIAMOND trial, 75 adults using CGM were randomized to either CGM+CSII or CGM+MDI (control) and surveyed at baseline and 28 weeks. We performed within-trial and lifetime cost-effectiveness analyses (CEAs) and estimated lifetime costs and quality-adjusted life-years (QALYs) via a modified Sheffield T1D model.

RESULTS

Within the trial, the CGM+CSII group had a significant reduction in quality of life from baseline (-0.02 ± 0.05 difference in difference [DiD]) compared with controls. Total per-person 28-week costs were $8,272 (CGM+CSII) versus $5,623 (CGM+MDI); the difference in costs was primarily attributable to pump use ($2,644). Pump users reduced insulin intake (-12.8 units DiD) but increased the use of daily number of test strips (+1.2 DiD). Pump users also increased time with glucose in range of 70 to 180 mg/dL but had a higher HbA1c (+0.13 DiD) and more nonsevere hypoglycemic events. In the lifetime CEA, CGM+CSII would increase total costs by $112,045 DiD, decrease QALYs by 0.71, and decrease life expectancy by 0.48 years.

CONCLUSIONS

Based on this single trial, initiating an insulin pump in adults with T1D already using CGM was associated with higher costs and reduced quality of life. Additional evidence regarding the clinical effects of adopting combinations of new technologies from trials and real-world populations is needed to confirm these findings.

Assessing the economic value of maintained improvements in Type 1 diabetes management, in terms of HbA1c , weight and hypoglycaemic event incidence

AIMS

Insulin therapy is indicated for people with Type 1 diabetes mellitus; however, treatment-related weight gain and hypoglycaemia represent barriers to optimal glycaemic management. This study assessed the health economic value of maintained reductions in HbA_1c , BMI and hypoglycaemia incidence among the UK Type 1 diabetes population.

METHODS

The Cardiff Type 1 Diabetes Model was used to estimate lifetime costs, life-years and quality-adjusted life-years (QALYs) for individuals with Type 1 diabetes at different baseline HbA_1c , BMI and hypoglycaemic event rates. Results were discounted at 3.5%, and the net monetary benefit associated with improving Type 1 diabetes management was derived at £20 000/QALY gained. Per-person outputs were inflated to national levels using UK Type 1 diabetes prevalence estimates.

RESULTS

Modelled subjects with an HbA_1c of 86 mmol/mol (10.0%) were associated with discounted lifetime per-person costs of £23 795; £12 649 of which may be avoided by maintaining an HbA_1c of 42 mmol/mol (6.0%). Combined with estimated QALY gains of 2.80, an HbA_1c of 42 mmol/mol (6.0%) vs. 86 mmol/mol (10.0%) was associated with a £68 621 per-person net monetary benefit. Over 1 year, unit reductions in BMI produced £120 per-person net monetary benefit, and up to £197 for the avoidance of one non-severe hypoglyceamic event.

CONCLUSIONS

Maintained reductions in HbA_1c significantly alleviate the burden associated with Type 1 diabetes in the UK. Given the influence of weight and hypoglycaemia on health economic outcomes, they must also be key considerations when assessing the value of Type 1 diabetes technologies in clinical practice.

Validation of the Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM)

BACKGROUND

The Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM) was developed to address study questions pertaining to the cost-effectiveness of treatment alternatives in the care of patients with type 2 diabetes mellitus (T2DM). Naturally, the usefulness of a model is determined by the accuracy of its predictions. A previous version of ECHO-T2DM was validated against actual trial outcomes and the model predictions were generally accurate. However, there have been recent upgrades to the model, which modify model predictions and necessitate an update of the validation exercises.

OBJECTIVES

The objectives of this study were to extend the methods available for evaluating model validity, to conduct a formal model validation of ECHO-T2DM (version 2.3.0) in accordance with the principles espoused by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM), and secondarily to evaluate the relative accuracy of four sets of macrovascular risk equations included in ECHO-T2DM.

METHODS

We followed the ISPOR/SMDM guidelines on model validation, evaluating face validity, verification, cross-validation, and external validation. Model verification involved 297 'stress tests', in which specific model inputs were modified systematically to ascertain correct model implementation. Cross-validation consisted of a comparison between ECHO-T2DM predictions and those of the seminal National Institutes of Health model. In external validation, study characteristics were entered into ECHO-T2DM to replicate the clinical results of 12 studies (including 17 patient populations), and model predictions were compared to observed values using established statistical techniques as well as measures of average prediction error, separately for the four sets of macrovascular risk equations supported in ECHO-T2DM. Sub-group analyses were conducted for dependent vs. independent outcomes and for microvascular vs. macrovascular vs. mortality endpoints.

RESULTS

All stress tests were passed. ECHO-T2DM replicated the National Institutes of Health cost-effectiveness application with numerically similar results. In external validation of ECHO-T2DM, model predictions agreed well with observed clinical outcomes. For all sets of macrovascular risk equations, the results were close to the intercept and slope coefficients corresponding to a perfect match, resulting in high R ² and failure to reject concordance using an F test. The results were similar for sub-groups of dependent and independent validation, with some degree of under-prediction of macrovascular events.

CONCLUSION

ECHO-T2DM continues to match health outcomes in clinical trials in T2DM, with prediction accuracy similar to other leading models of T2DM.

How Consistent is the Relationship between Improved Glucose Control and Modelled Health Outcomes for People with Type 2 Diabetes Mellitus? a Systematic Review

BACKGROUND

There are an increasing number of studies using simulation models to conduct cost-effectiveness analyses for type 2 diabetes mellitus.

OBJECTIVE

To evaluate the relationship between improvements in glycosylated haemoglobin (HbA_1c) and simulated health outcomes in type 2 diabetes cost-effectiveness studies.

METHODS

A systematic review was conducted on MEDLINE and EMBASE to collect cost-effectiveness studies using type 2 diabetes simulation models that reported modelled health outcomes of blood glucose-related interventions in terms of quality-adjusted life-years (QALYs) or life expectancy (LE). The data extracted included information used to characterise the study cohort, the intervention's treatment effects on risk factors and model outcomes. Linear regressions were used to test the relationship between the difference in HbA_1c (∆HbA_1c) and incremental QALYs (∆QALYs) or LE (∆LE) of intervention and control groups. The ratio between the ∆QALYs and ∆LE was calculated and a scatterplot between the ratio and ∆HbA_1c was used to explore the relationship between these two.

RESULTS

Seventy-six studies were included in this research, contributing to 124 pair of comparators. The pooled regressions indicated that the marginal effect of a 1% HbA_1c decrease in intervention resulted in an increase in life-time QALYs and LE of 0.371 (95% confidence interval 0.286-0.456) and 0.642 (95% CI 0.494-0.790), respectively. No evidence of heterogeneity between models was found. An inverse exponential relationship was found and fitted between the ratio (∆QALY/∆LE) and ∆HbA_1c.

CONCLUSION

There is a consistent relationship between ∆HbA_1c and ∆QALYs or ∆LE in cost-effectiveness analyses using type 2 diabetes simulation models. This relationship can be used as a diagnostic tool for decision makers.

Personalized support for chronic conditions. A novel approach for enhancing self-management and improving lifestyle

OBJECTIVE

Solutions for improving management of chronic conditions are under the attention of healthcare systems, due to the increasing prevalence caused by demographic change and better survival, and the relevant impact on healthcare expenditures. The objective of this study was to propose a comprehensive architecture of a mHealth system aimed at boosting the active and informed participation of patients in their care process, while at the same time overcoming the current technical and psychological/clinical issues highlighted by the existing literature.

METHODS

After having studied the current challenges outlined in the literature, both in terms of technological and human requirements, we focused our attention on some specific psychological aspects with a view to providing patients with a comprehensive and personalized solution. Our approach has been reinforced through the results of a preliminary assessment we conducted on 22 patients with chronic conditions. The main goal of such an assessment was to provide a preliminary understanding of their needs in a real context, both in terms of self-awareness and of their predisposition toward the use of IT solutions.

RESULTS

According to the specific needs and features, such as mindfulness and gamification, which were identified through the literature and the preliminary assessment, we designed a comprehensive open architecture able to provide a tailor-made solution linked to specific individuals' needs.

CONCLUSION

The present study represents the preliminary step towards the development of a solution aimed at enhancing patients' actual perception and encouraging self-management and self-awareness for a better lifestyle. Future work regards further identification of pathology-related needs and requirements through focus groups including all stakeholders in order to describe the architecture and functionality in greater detail.

A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus

BACKGROUND

Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM.

OBJECTIVES

This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM.

METHODS

A systematic review of Embase(®), MEDLINE(®), MEDLINE(®) In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities.

RESULTS

We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis.

CONCLUSIONS

Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of 'best in class' methods for the different technical aspects of T1DM modelling.

Methods applied in cost-effectiveness models for treatment strategies in type 2 diabetes mellitus and their use in Health Technology Assessments: a systematic review of the literature from 2008 to 2013

OBJECTIVE

To identify and compare health-economic models that were developed to evaluate the cost-effectiveness of treatments for type 2 diabetes mellitus (T2DM), and their use within Health Technology Assessments (HTAs).

METHODS

In total, six commonly used databases were searched for articles published between October 2008 and January 2013, using a protocolized search strategy and inclusion criteria. The websites of HTA organizations in nine countries, and proceedings from five relevant conferences, were also reviewed. The identified new health-economic models were qualitatively assessed using six criteria that were developed based on technical components, and characteristics related to the disease or the treatments being assessed. Finally, the number of times the models were applied within HTA reports, published literature, and/or major conferences was determined.

RESULTS

Thirteen new models were identified and reviewed in depth. Most of these were based on identical key data sources, and applied a similar model structure, either using Markov modeling or microsimulation techniques. The UKPDS equations and panel regressions were frequently used to estimate the occurrence of diabetes-related complications and the probability of developing risk factors in the long term. The qualitative assessment demonstrated that the CARDIFF, Sheffield T2DM and ECHO T2DM models seem technically equipped to appropriately assess the long-term health-economic consequences of chronic treatments for patients with T2DM. It was observed that the CORE model is the most widely described in literature and conferences, and the most often applied model within HTA submissions, followed by the CARDIFF and UKPDS models.

CONCLUSION

This research provides an overview of T2DM models that were developed between 2008 and January 2013. The outcomes of the qualitative assessments, combined with frequent use in local reimbursement decisions, prove the applicability of the CORE, CARDIFF and UKPDS models to address decision problems related to the long-term clinical and economic consequences of new and existing T2DM treatments.

How Do Diabetes Models Measure Up? A Review of Diabetes Economic Models and ADA Guidelines

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total of 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion.

Development and validation of risk prediction equations to estimate future risk of blindness and lower limb amputation in patients with diabetes: cohort study

STUDY QUESTION

Is it possible to develop and externally validate risk prediction equations to estimate the 10 year risk of blindness and lower limb amputation in patients with diabetes aged 25-84 years?

METHODS

This was a prospective cohort study using routinely collected data from general practices in England contributing to the QResearch and Clinical Practice Research Datalink (CPRD) databases during the study period 1998-2014. The equations were developed using 763 QResearch practices (n=454,575 patients with diabetes) and validated in 254 different QResearch practices (n=142,419) and 357 CPRD practices (n=206,050). Cox proportional hazards models were used to derive separate risk equations for blindness and amputation in men and women that could be evaluated at 10 years. Measures of calibration and discrimination were calculated in the two validation cohorts.

STUDY ANSWER AND LIMITATIONS

Risk prediction equations to quantify absolute risk of blindness and amputation in men and women with diabetes have been developed and externally validated. In the QResearch derivation cohort, 4822 new cases of lower limb amputation and 8063 new cases of blindness occurred during follow-up. The risk equations were well calibrated in both validation cohorts. Discrimination was good in men in the external CPRD cohort for amputation (D statistic 1.69, Harrell's C statistic 0.77) and blindness (D statistic 1.40, Harrell's C statistic 0.73), with similar results in women and in the QResearch validation cohort. The algorithms are based on variables that patients are likely to know or that are routinely recorded in general practice computer systems. They can be used to identify patients at high risk for prevention or further assessment. Limitations include lack of formally adjudicated outcomes, information bias, and missing data.

WHAT THIS STUDY ADDS

Patients with type 1 or type 2 diabetes are at increased risk of blindness and amputation but generally do not have accurate assessments of the magnitude of their individual risks. The new algorithms calculate the absolute risk of developing these complications over a 10 year period in patients with diabetes, taking account of their individual risk factors.

FUNDING, COMPETING INTERESTS, DATA SHARING

JH-C is co-director of QResearch, a not for profit organisation which is a joint partnership between the University of Nottingham and Egton Medical Information Systems, and is also a paid director of ClinRisk Ltd. CC is a paid consultant statistician for ClinRisk Ltd.

The Michigan Model for Coronary Heart Disease in Type 2 Diabetes: Development and Validation

OBJECTIVES

The aim of this study was to develop and validate a computer simulation model for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) that reflects current medical and surgical treatments.

RESEARCH DESIGN AND METHODS

We modified the structure of the CHD submodel in the Michigan Model for Diabetes to allow for revascularization procedures before and after first myocardial infarction, for repeat myocardial infarctions and repeat revascularization procedures, and for congestive heart failure. Transition probabilities that reflect the direct effects of medical and surgical therapies on outcomes were derived from the literature and calibrated to recently published population-based epidemiologic studies and randomized controlled clinical trials. Monte Carlo techniques were used to implement a discrete-state and discrete-time multistate microsimulation model. Performance of the model was assessed using internal and external validation. Simple regression analysis (simulated outcome=b(0)+b(1)×published outcome) was used to evaluate the validation results.

RESULTS

For the 21 outcomes in the six studies used for internal validation, R(2) was 0.99, and the slope of the regression line was 0.98. For the 16 outcomes in the five studies used for external validation, R(2) was 0.81, and the slope was 0.84.

CONCLUSIONS

Our new computer simulation model predicted the progression of CHD in patients with T2DM and will be incorporated into the Michigan Model for Diabetes to assess the cost-effectiveness of alternative strategies to prevent and treat T2DM.

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes

BACKGROUND

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms.

METHODS

. Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients.

RESULTS

. The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective.

LIMITATIONS

. The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power.

CONCLUSIONS

. By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.

Improving management of type 1 diabetes in the UK: the Dose Adjustment For Normal Eating (DAFNE) programme as a research test-bed. A mixed-method analysis of the barriers to and facilitators of successful diabetes self-management, a health economic analysis, a cluster randomised controlled trial of different models of delivery of an educational intervention and the potential of insulin pumps and additional educator input to improve outcomes

Background

Many adults with type 1 diabetes cannot self-manage their diabetes effectively and die prematurely with diabetic complications as a result of poor glucose control. Following the positive results obtained from a randomised controlled trial (RCT) by the Dose Adjustment For Normal Eating (DAFNE) group, published in 2002, structured training is recommended for all adults with type 1 diabetes in the UK.

Aim

With evidence that blood glucose control is not always improved or sustained, we sought to determine factors explaining why some patients benefit from training more than other patients, identifying barriers to successful self-management, while developing other models to make skills training more accessible and effective.

Findings

We confirmed that glycaemic outcomes are not always improved or sustained when the DAFNE programme is delivered routinely, although improvements in psychosocial outcomes are maintained. DAFNE courses and follow-up support is needed to help participants instil and habituate key self-management practices such as regular diary/record keeping. DAFNE graduates need structured professional support following training. This is currently either unavailable or provided ad hoc without a supporting evidence base. Demographic and psychosocial characteristics had minimal explanatory power in predicting glycaemic control but good explanatory power in predicting diabetes-specific quality of life over the following year. We developed a DAFNE course delivered for 1 day per week over 5 weeks. There were no major differences in outcomes between this and a standard 1-week DAFNE course; in both arms of a RCT, glycaemic control improved by less than in the original DAFNE trial. We piloted a course delivering both the DAFNE programme and pump training. The pilot demonstrated the feasibility of a full multicentre RCT and resulted in us obtaining subsequent Health Technology Assessment programme funding. In collaboration with the National Institute for Health Research (NIHR) Diabetes Research Programme at King’s College Hospital (RG-PG-0606-1142), London, an intervention for patients with hypoglycaemic problems, DAFNE HART (Dose Adjustment for Normal Eating Hypoglycaemia Awareness Restoration Training), improved impaired hypoglycaemia awareness and is worthy of a formal trial. The health economic work developed a new type 1 diabetes model and confirmed that the DAFNE programme is cost-effective compared with no structured education; indeed, it is cost-saving in the majority of our analyses despite limited glycated haemoglobin benefit. Users made important contributions but this could have been maximised by involving them with grant writing, delaying training until the group was established and funding users’ time off work to maximise attendance. Collecting routine clinical data to conduct continuing evaluated roll-out is possible but to do this effectively requires additional administrator support and/or routine electronic data capture.

Conclusions

We propose that, in future work, we should modify the current DAFNE curricula to incorporate emerging understanding of behaviour change principles to instil and habituate key self-management behaviours that include key DAFNE competencies. An assessment of numeracy, critical for insulin dose adjustment, may help to determine whether or not additional input/support is required both before and after training. Models of structured support involving professionals should be developed and evaluated, incorporating technological interventions to help overcome the barriers identified above and enable participants to build effective self-management behaviours into their everyday lives.

Trial registration

ClinicalTrials.gov NCT01069393.

Funding

The NIHR Programme Grants for Applied Research programme.

Assessing the cost-effectiveness of type 1 diabetes interventions: the Sheffield type 1 diabetes policy model

AIMS

To build a flexible and comprehensive long-term type 1 diabetes mellitus model incorporating the most up-to-date methodologies to allow a number of cost-effectiveness evaluations.

METHODS

This paper describes the conceptual modelling, model implementation and model validation of the Sheffield type 1 diabetes policy model (version 1.0), developed through funding by the U.K. National Institute for Health Research as part of the Dose Adjustment for Normal Eating research programme. The model is an individual patient-level simulation model of type 1 diabetes and it includes long-term microvascular (retinopathy, neuropathy and nephropathy) and macrovascular (myocardial infarction, stroke, revascularization and angina) diabetes-related complications and acute adverse events (severe hypoglycaemia and diabetic ketoacidosis). The occurrence of these diabetes-related complications in the model is linked to simulated individual patient-level risk factors, including HbA1c , age, duration of diabetes, lipids and blood pressure. Transition probabilities were modelled based on a combination of existing risk functions, published trials, epidemiological studies and individual-level data from the Dose Adjustment for Normal Eating research programme.

RESULTS

The model takes a lifetime perspective, estimating the impact of interventions on costs, clinical outcomes, survival and quality-adjusted life years. Validation of the model suggested that, for almost all diabetes-related complications predicted, event rates were within 10% of the normalized rates reported in the studies used to build the model.

CONCLUSIONS

The model is highly flexible and has broad potential application to evaluate the Dose Adjustment for Normal Eating research programme, other structured diabetes education programmes and other interventions for type 1 diabetes.

Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM)

OBJECTIVE

This study constructed the Economic and Health Outcomes Model for type 2 diabetes mellitus (ECHO-T2DM), a long-term stochastic microsimulation model, to predict the costs and health outcomes in patients with T2DM. Naturally, the usefulness of the model depends upon its predictive accuracy. The objective of this work is to present results of a formal validation exercise of ECHO-T2DM.

METHODS

The validity of ECHO-T2DM was assessed using criteria recommended by the International Society for Pharmacoeconomics and Outcomes Research/Society for Medical Decision Making (ISPOR/SMDM). Specifically, the results of a number of clinical trials were predicted and compared with observed study end-points using a scatterplot and regression approach. An F-test of the best-fitting regression was added to assess whether it differs statistically from the identity (45°) line defining perfect predictions. In addition to testing the full model using all of the validation study data, tests were also performed of microvascular, macrovascular, and survival outcomes separately. The validation tests were also performed separately by type of data (used vs not used to construct the model, economic simulations, and treatment effects).

RESULTS

The intercept and slope coefficients of the best-fitting regression line between the predicted outcomes and corresponding trial end-points in the main analysis were -0.0011 and 1.067, respectively, and the R(2) was 0.95. A formal F-test of no difference between the fitted line and the identity line could not be rejected (p = 0.16). The high R(2) confirms that the data points are closely (and linearly) associated with the fitted regression line. Additional analyses identified that disagreement was highest for macrovascular end-points, for which the intercept and slope coefficients were 0.0095 and 1.225, respectively. The R(2) was 0.95 and the estimated intercept and slope coefficients were 0.017 and 1.048, respectively, for mortality, and the F-test was narrowly rejected (p = 0.04). The sub-set of microvascular end-points showed some tendency to over-predict (the slope coefficient was 1.095), although concordance between predictions and observed values could not be rejected (p = 0.16).

LIMITATIONS

Important study limitations include: (1) data availability limited one to tests based on end-of-study outcomes rather than time-varying outcomes during the studies analyzed; (2) complex inclusion and exclusion criteria in two studies were difficult to replicate; (3) some of the studies were older and reflect outdated treatment patterns; and (4) the authors were unable to identify published data on resource use and costs of T2DM suitable for testing the validity of the economic calculations.

CONCLUSIONS

Using conventional methods, ECHO-T2DM simulated the treatment, progression, and patient outcomes observed in important clinical trials with an accuracy consistent with other well-accepted models. Macrovascular outcomes were over-predicted, which is common in health-economic models of diabetes (and may be related to a general over-prediction of event rates in the United Kingdom Prospective Diabetes Study [UKPDS] Outcomes Model). Work is underway in ECHO-T2DM to incorporate new risk equations to improve model prediction.

Development of an economic evaluation of diagnostic strategies: the case of monogenic diabetes

OBJECTIVES

To describe the development process for defining an appropriate model structure for the economic evaluation of test-treatment strategies for patients with monogenic diabetes (caused by mutations in the GCK, HNF1A or HNF4A genes).

DESIGN

Experts were consulted to identify and define realistic test-treatment strategies and care pathways. A systematic assessment of published diabetes models was undertaken to inform the model structure.

SETTING

National Health Service in England and Wales.

PARTICIPANTS

Experts in monogenic diabetes whose collective expertise spans the length of the patient care pathway.

PRIMARY AND SECONDARY OUTCOMES

A defined model structure, including the test-treatment strategies, and the selection of a published diabetes model appropriate for the economic evaluation of strategies to identify patients with monogenic diabetes.

RESULTS

Five monogenic diabetes test-treatment strategies were defined: no testing of any kind, referral for genetic testing based on clinical features as noted by clinicians, referral for genetic testing based on the results of a clinical prediction model, referral for genetic testing based on the results of biochemical and immunological tests, referral for genetic testing for all patients with a diagnosis of diabetes under the age of 30 years. The systematic assessment of diabetes models identified the IMS CORE Diabetes Model (IMS CDM) as a good candidate for modelling the long-term outcomes and costs of the test-treatment strategies for monogenic diabetes. The short-term test-treatment events will be modelled using a decision tree which will feed into the IMS CDM.

CONCLUSIONS

Defining a model structure for any economic evaluation requires decisions to be made. Expert consultation and the explicit use of critical appraisal can inform these decisions. Although arbitrary choices have still been made, decision modelling allows investigation into such choices and the impact of assumptions that have to be made due to a lack of data.

Towards renewed health economic simulation of type 2 diabetes: risk equations for first and second cardiovascular events from Swedish register data

OBJECTIVE

Predicting the risk of future events is an essential part of health economic simulation models. In pursuit of this goal, the current study aims to predict the risk of developing first and second acute myocardial infarction, heart failure, non-acute ischaemic heart disease, and stroke after diagnosis in patients with type 2 diabetes, using data from the Swedish National Diabetes Register.

MATERIAL AND METHODS

Register data on 29,034 patients with type 2 diabetes were analysed over five years of follow up (baseline 2003). To develop and validate the risk equations, the sample was randomly divided into training (75%) and test (25%) subsamples. The Weibull proportional hazard model was used to estimate the coefficients of the risk equations, and these were validated in both the training and the test samples.

RESULTS

In total, 4,547 first and 2,418 second events were observed during the five years of follow up. Experiencing a first event substantially elevated the risk of subsequent events. There were heterogeneities in the effects of covariates within as well as between events; for example, while for females the hazard ratio of having a first acute myocardial infarction was 0.79 (0.70-0.90), the hazard ratio of a second was 1.21 (0.98-1.48). The hazards of second events decreased as the time since first events elapsed. The equations showed adequate calibration and discrimination (C statistics range: 0.70-0.84 in test samples).

CONCLUSION

The accuracy of health economic simulation models of type 2 diabetes can be improved by ensuring that they account for the heterogeneous effects of covariates on the risk of first and second cardiovascular events. Thus it is important to extend such models by including risk equations for second cardiovascular events.

Use of Secondary Data to Estimate Instantaneous Model Parameters of Diabetic Heart Disease: Lemonade Method

With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single "best" study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where 1) the theoretical model may include an instantaneous state of distinct interest to the researchers, and 2) the study design may be such that study data can not be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of "If life gives you lemon, make lemonade", we call this method "Lemonade Method". Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.

Hospitalization costs for heart failure in people with type 2 diabetes: cost-effectiveness of its prevention measured by a simulated preventive treatment

OBJECTIVES

To estimate the cost-consequence of interventions to prevent hospitalizations for heart failure (HF) in people with type 2 diabetes.

METHODS

In HF events (63) from type 2 diabetes-related hospitalizations (N = 462) recorded in an Argentine hospital (March 2004-April 2005), we verified 1) the presence of one metabolic HF predictor (glycosylated hemoglobin [HbA1c] value) before hospitalization; and 2) in a simulation model, the resources needed for its prevention controlling such predictor during 6 months before and after the event. Sensitivity analysis of HF risk reduction, hospitalization cost, and cost of different treatments to achieve HbA1c 7% or less was performed with a Monte Carlo simulation (10,000 iterations).

RESULTS

HF represented 14% of hospitalizations, with a 44% rehospitalization rate for the same cause. Due to the total estimated cost for an HF hospitalization event was $437.31, the prevention attained using our simulated treatment was $2326.51. The number needed to treat to prevent an HF event under any of the proposed alternatives to reduce HbA1c would be 3.57 (95% confidence interval 2.00-16.67). The additional cost of the simulated treatment versus the real one oscillates between $6423.91 and $8455.68.

CONCLUSIONS

HbA1c control to reduce the number of HF events would be economically beneficial for health care payers.

Economic models in type 2 diabetes

OBJECTIVE

To identify and critically appraise cost-effectiveness models developed to evaluate type 2 diabetes (T2D) treatments and to assess which types of treatment effects they capture.

RESEARCH DESIGN AND METHODS

A systematic search was performed in MEDLINE, EMBASE, Centre for Reviews and Dissemination databases at the University of York, and Health Economic Evaluation Database for the period to September 2008. The websites of Health Technology Assessment (HTA) bodies in different countries were also screened for relevant models. For each of the identified original models, details of the structure, data in- and outputs were extracted and the overall quality of the model in terms of the combination of structure, assumptions and data inputs were appraised using published criteria.

RESULTS

Seventy-eight articles and 41 HTAs reporting relevant economic evaluations were identified. There were ten models with multiple publications, and a further ten models with one associated publication. The critical review demonstrated that most had the same fundamental structure, used similar micro-simulation techniques and were based on the same key data sources. However, the process for identification of relevant data and their synthesis, and the selection of outcomes lacked transparency. The models differed according to the extent and type of interventions they evaluated and which diabetes complications and treatment-related adverse events were captured. For example, just one model incorporated changes in patient weight, despite the fact that weight gain can be a side-effect of some treatments, and weight loss a potential benefit of others.

CONCLUSIONS

Whilst many economic models exist in T2D, most share common features such as the model type. Identified shortcomings are lack of transparency in data identification and evidence synthesis as well as the selection of the modelled outcomes. Future models should aim to include all relevant treatment outcomes, whether these relate to effects on underlying diabetes and its complications or to short- or long-term side effects of treatment.

Chronic disease modeling and simulation software

Computers allow describing the progress of a disease using computerized models. These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression. To make this forecast reliable, good models and therefore good modeling tools are required. This paper will describe a new computer tool designed for chronic disease modeling. The modeling capabilities of this tool were used to model the Michigan model for diabetes. The modeling approach and its advantages such as simplicity, availability, and transparency are discussed.

A review of methods used in long-term cost-effectiveness models of diabetes mellitus treatment

Diabetes mellitus is a major healthcare concern from both a treatment and a funding perspective. Although decision makers frequently rely on models to evaluate the long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the methods used in long-term cost-effectiveness models of diabetes treatment. The following databases were searched up to April 2008 to identify published economic models evaluating treatments for diabetes mellitus: OVID MEDLINE, EMBASE and the Thomson's Biosis Previews, NHS EED via Wiley's Cochrane Library, and Wiley's HEED database. Identified articles were reviewed and grouped according to unique models. When a model was applied in different settings (e.g. country) or compared different treatment alternatives, only the original publication describing the model was included. In some cases, subsequent articles were included if they provided methodological advances from the original model. The following data were captured for each study: (i) study characteristics; (ii) model structure; (iii) long-term complications, data sources, methods reporting and model validity; (iv) utilities, data sources and methods reporting; (v) costs, data sources and methods reporting; (vi) model data requirements; and (vii) economic results including methods to deal with uncertainty. A total of 17 studies were identified, 12 of which allowed for the conduct of a cost-effectiveness analysis and a cost-utility analysis. Although most models were Markov-based microsimulations, models differed with respect to the number of diabetes-related complications included. The majority of the studies used a lifetime time horizon and a payer perspective. The DCCT for type 1 diabetes and the UKPDS for type 2 diabetes were the trial data sources most commonly cited for the efficacy data, although several non-randomized data sources were used. While the methods used to derive the efficacy data were commonly reported, less information was given regarding the derivation of the utilities or the costs. New interventions were generally deemed cost effective based on ten studies presenting results. Authors relied mostly on univariate sensitivity analyses to test the robustness of their models. Although diabetes is a complex disease, several models have been developed to predict the long-term costs and consequences associated with diabetes treatment. Combined with previous findings, this review supports the development of a reference case that could be used for any new diabetes models. Models could be enhanced if they had the capacity to deal with both first- and second-order uncertainty. Future research should continue to compare economic models for diabetes treatment in terms of clinical outcomes, costs and QALYs when applicable.

Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus

AIM

To ascertain if those with diabetes (and their carers) ascribe a similar level of risk to blood glucose control as healthcare professionals.

METHODS

We used a structured questionnaire to ask fifty healthcare professionals how 'dangerous' a given blood glucose value was. Their answers were modelled to produce an algorithm of assessed risk. To examine if patients (and their carers) would apportion a similar level of risk to that of healthcare professionals, the same questionnaire was issued to fifty children and adolescents with Type 1 diabetes. For patients under 8 years old the carers completed the questionnaires (n = 23). Both patient and carers together completed the questionnaire for those aged 8-11 years (n = 15) and patients over the age of 11 years completed the questionnaire themselves (n = 12). The median results and interquartile range of the assessed level of risk, as determined by the two groups, were compared using a generalized linear model.

RESULTS

A significant difference (P < 0.0001) was identified between the median risk assessments of the two groups. The zero level of assessed risk was upward shifted in the patient group by 0.8 mmol/l and indicated the patients' view of risk increased.

CONCLUSIONS

Patients with Type 1 diabetes (and their carers) evaluate the risk from blood glucose values differently from healthcare professionals. The euglycaemic state (zero ascribed risk) that patients chose was 0.8 mmol/l greater than that of healthcare professionals, indicating, perhaps, hypoglycaemia avoidance, a more pragmatic approach or less exposure to current trends in glycaemic control.

Pharmaco-economic issues for diabetes therapy

A systematic review was undertaken to analyse pharmaco-economic issues in diabetes, with evidence selected on the basis of relevance and immediacy. Pharmaco-economics in diabetes primarily relates to making choices about antidiabetic pharmaceuticals, and this is being influenced by global trends. Trends include increasing numbers of patients with diabetes, with increasing costs of caring for people with diabetes, and an ever-present focus on the costs of pharmaceuticals which are predicted to increase as the pace of development of new medications parallels the increasing incidence of the condition. These developments have influenced the demand for health care in diabetes in the last decade, and will continue to determine this in the coming decade. Recent national experiences are cited to illustrate current issues and to focus specifically upon the challenges facing a raft of new diabetes treatment options now hitting the marketplace, although supported by fewer completed long-term trials. It can be anticipated that these newer agents will be appraised for their cost-effectiveness or value for money. Economic analyses for some of the new technologies are summarized; in general, the peer-reviewed publications using well-accepted and validated models have reported that these technologies are cost-effective. Endorsement of any technology in a national setting is not awarded simply because the incremental cost-effectiveness ratio (ICER) falls below the threshold regarded as value for money. In most national observations the reviewers expressed concerns about assumptions used in economic modelling which resulted in the ICERs being deemed optimistic at best, generally highly uncertain, and resulting in the cost-effectiveness appearing better than it really would be in clinical practice. This has often led to the authorities concluding that the price advantage of new technologies over comparators could not be justified, essentially leading to restrictions in use compared to their licence. In general, a paucity of robust evidence on longer-term outcome data together with a lack of health-related quality of life (HRQOL) data collected in a reliable manner in appropriate patients and amenable to utility (and hence quality adjusted life year or QALY) estimation have resulted in problems for these new drugs at the so-called fourth (cost-effectiveness) hurdle. In the light of these findings, the implications for generating credible fit-for-purpose cost-effectiveness analyses of new technologies in diabetes are discussed. Throughout this chapter, the interested reader is referred to a number of excellent review articles for further details.

Computer modeling of diabetes and its complications: a report on the Fourth Mount Hood Challenge Meeting

Computer simulation models are mathematical equations combined in a structured framework to represent some real or hypothetical system. One of their uses is to allow the projection of short-term data from clinical trials to evaluate clinical outcomes and costs over a long-term period. This technology is becoming increasingly important to assist decision making in modern medicine in situations where there is a paucity of long-term clinical trial data, as recently acknowledged in the American Diabetes Association Consensus Panel Guidelines for Computer Modeling of Diabetes and its Complications. The Mount Hood Challenge Meetings provide a forum for computer modelers of diabetes to discuss and compare models and identify key areas of future development to advance the field. The Fourth Mount Hood Challenge in 2004 was the first meeting of its kind to ask modelers to perform simulations of outcomes for patients in published clinical trials, allowing comparison against "real life" data. Eight modeling groups participated in the challenge. Each group was given three of the following challenges: to simulate a trial of type 2 diabetes (CARDS [Collaborative Atorvastatin Diabetes Study]); to simulate a trial of type 1 diabetes (DCCT [Diabetes Control and Complications Trial]); and to calculate outcomes for a hypothetical, precisely specified patient (cross-model validation). The results of the models varied from each other and for methodological reasons, in some cases, from the published trial data in important ways. This approach of performing systematic comparisons and validation exercises has enabled the identification of key differences among the models, as well as their possible causes and directions for improvement in the future.