Sentence examples for model calibration slope from inspiring English sources

Backwards elimination also produced an overfitted model (calibration slope 0.77) with similar discrimination (ROC area 0.795).

Standard regression produced an overfitted model (calibration slope 0.76 (95% confidence interval 0.65 to 0.99)), whereas the models from ridge and lasso demonstrated far better calibration (calibration slopes of 1.01 and 0.94, respectively).

For the KORA basic model, AUSDRISK, and EPIC-Norfolk model, calibration slopes were close to 1.0, but those were smaller or larger than 1.0 for other logistic regression models (tables 2 and 3 ).

Calibration of the model resulted in a calibration slope of 1.08 and an intercept of −0.06.

If the model is well calibrated, the estimated slope (or calibration slope) should be close to 1, whereas an overfitted model would have a slope much less than 1, indicating that low risks are underestimated and high risks are overestimated.

When the PI is the linear predictor from a model (the usual situation), the calibration slope is by construction exactly 1 in the derivation dataset.

In the simplest form of internal validation for the performance measure of a calibration slope: The model is fitted to each bootstrap dataset The estimated coefficients are used to obtain predictions for the patients in the original dataset These predictions are used to calculate the calibration slope for the fitted model.

In simulation studies, it has been seen that use of the incorrect marginal risk calculation from random effects models results in poorly calibrated overall marginal predictions (calibration slope <1 and calibration in the large ≠ 0) with mis-calibration becoming worse with higher degrees of clustering.

The calibration and discrimination both showed very reasonable results for the prediction model (C-index = 0.720 and calibration slope for the prognostic index = 1.025, for the 10 year survival).

We used these linear predictors in a logistic regression model to test whether the prediction rule was well calibrated, resulting in a calibration slope and an intercept.

The calibration slope of the temporal model was 1.2, and the intercept was 0.22.