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[10] We used conventional logistic regression to generate predicted probabilities of TB, plotted the fraction of true positives versus the fraction of false positives at various thresholds of predicted probability as a receiver operating characteristic curve, and measured model accuracy using the concordance (C) statistic (area under the curve).
To express the performance of the logistic models the AUC statistic (area under the curve) is used.
We assessed model fit using max-rescaled R2 and the c statistic (area under the ROC curve).
The C statistic (area under the curve) to assess model fit for all physical function limitations (model 4 for each) was >0.72, confirming good model fit.
The goodness of fit and the discrimination of the model were determined by the Hosmer-Lemeshow statistic and the c statistic (area under the curve), respectively.
The c statistic (area under the ROC curve) for the treatment results in the breast cancer spheroid model was 0.86 (Fig. 3a), which was superior to classic risk factors.
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When added to the TIMI STEMI Risk Score alone, the multimarker risk score significantly improved the C-statistic (area under the curve, 0.75 [95% CI, 0.69-0.81] to 0.82 [0.78-0.87]; P=0.001), net reclassification index (0.93; P<0.001), and integrated discrimination index (0.09; P<0.091).
The C-statistic (area under the receiver operating characteristic curve) was 0.811.
The final model was evaluated by c-statistic (area under the ROC curve) and the Hosmer and Lemeshow fit test.
C-statistic (area under the ROC Curve) was calculated as a measure of the models' ability to predict mortality.
The cross-walk versions were evaluated using logistic regression, predicting a new pressure ulcer at the next quarterly assessment, with the C-statistic (area under the receiver operator characteristic curve) as the main evaluation criterion.
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