288 research outputs found
Development of a high capacity toroidal Ni/Cd cell
A nickel cadmium battery design which can offer better thermal management, higher energy density and much lower cost than the state-of-the-art is emphasized. A toroidal Ni/Cd cell concept is described. It was critically reviewed and used to develop two cell designs for practical implementation. One is a double swaged and the other a swaged welded configuration
PDB79 Contrasting cost effectiveness results derived from the ukpds 68 and 82 risk equations in type 2 diabetes
PRM37 Impact Of Single Risk Factor Changes On Long-Term Outcomes And Cost In A Type 2 Diabetes Modeling Study Contrasting Projections With Ukpds68, Swedish National Diabetes Registry And The Advance Risk Equations
PDB81 Validating the ukpds 82 risk equations to contemporary outcomes studies in type 2 diabetes
PRM17 Quantifying Nonlinear Effects in Stochastic Markov Simulation Using UKPDS 68 and Ukpds 82 Equations in Type 2 Diabetes Modeling Analysis With the IMS Core Diabetes Model (CDM)
PRM87 All-Cause Mortality Validation of the Core Diabetes Model Against Predictions of the Charlson Comorbidity Index
CV3 LONG-TERM COSTS AND CLINICAL CONSEQUENCES OF DIFFERENT LEVELS OF 2-HOUR POST PRANDIAL BLOOD GLUCOSE IN NON-DIABETES PATIENTS
PRM69 Correlating Cost Effectiveness Output with Patient Level Data Input Via the IMS Core Diabetes Model (CDM)
PDB32 Quantifying the Direct and Indirect Costs Associated with Severe and Non-Severe Hypoglycaemia in Subjects with Type-2 Diabetes Who Are Treated with Insulin
Computer modeling of diabetes and Its transparency: a report on the Eighth Mount Hood Challenge
Objectives
The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.
Methods
Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.
Results
Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.
Conclusions
Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results
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