Comparison of strategies for validating binary logistic regression models Live sex chat without login


07-Aug-2017 08:22

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Discrimination measures how well the model can distinguish between patients who die and those who survive.

Discrimination is usually assessed by the area under the receiver operating characteristic curve (AU-ROC) [].

Calibration measures the model’s ability to generate predictions that are on average close to the average observed outcome.

Calibration has traditionally been approached in two steps.

On the basis of the results of Poole et al., changes over time may be a possible interpretation as to why the older score is not considered better than the more recent one.

In this way, continuous improvements in health care possibly have a bigger impact on sicker patients, whereas unchanged conventional treatments have been given to low-risk patients since a long time ago.

Like in the present study, both SAPS II and SAPS 3 have shown adequate inter-rater reliability, but the standardized mortality ratios are still likely to be influenced by the rater’s scoring practice [].

Thus, conclusions of this new study do not substantially differ from previous ones.

On the contrary, miscalibration of the model will be a function of expected probability.

It would have been better if the calibration belt had been tested along with the traditional approach in the present study in order to measure calibration in a simultaneous way.



Mar 12, 1998. Comparison of Strategies for Validating Binary Logistic. Regression Models. Frank E Harrell Jr. Division of Biostatistics and Epidemiology.… continue reading »


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For example, over-fitting can occur when a model which was initially fit with the. Comparisons of strategies for validating binary logistic regression models.… continue reading »


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