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Model discrimination between survivors and non-survivors was excellent.
However, the multilevel models showed high C statistics (up to 0.90) indicating good model discrimination between subjects for each level of the outcome.
Predictive performance (model discrimination between deaths and survivors) was significantly better than that of the TRISS with the area under the receiver operating characteristic curve (AROC) value of 0.947 (95% CI 0.943 to 0.951) on the prediction set and 0.952 955% CI 0.946 to 0.957) on the validation set.
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Model discrimination was compared between models using chi-square tests for differences between c-statistics according to the methods for comparing the areas under two correlated ROC curves suggested by DeLong and colleagues [ 19].
The paper introduces DT-optimum designs that provide a specified balance between model discrimination and parameter estimation.
This paper introduces the CDT-optimum design that provides a specified balance between model discrimination, parameter estimation and estimation of a parametric function such as the area under curve in models for drug absorbance.
Differences between the three model discrimination methods are discussed along with differences between the three copolymer measurements that have been studied.
The use of nonseparable activity and kinetics for model discrimination based on structural differences between mechanisms is also discussed.
We assessed model discrimination using the C statistic for predictive value and compared the difference between basic models and models including PA28γ expression (Table S4).
The accuracy of a severity-of-illness score can be assessed by the model's discrimination between survivors and nonsurvivors (how well the model predicts the correct outcome) and by assessing calibration (how well the model tracks outcomes across the range of possible scores).
Agreement between calculated and experimentally determined eigenfunctions testifies to the validity of the present method of model discrimination.
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