The calibration belt is a graphical approach designed to evaluate the goodness of fit of
binary outcome models such as logistic regression models. The calibration belt examines …

Calibration is one of the main properties that must be accomplished by any predictive model.
Overcoming the limitations of many approaches developed so far, a study has recently …

Assessing the calibration of methods for estimating the probability of the occurrence of a
binary outcome is an important aspect of validating the performance of riskprediction …

Calibration in binary prediction models, that is, the agreement between model predictions
and observed outcomes, is an important aspect of assessing the models' utility for …

A prognostic model is well calibrated when it accurately predicts event rates. This is first
determined by testing for goodness of fit with the development dataset. All existing tests and …

Before a logistic regression model is used to describe the relationship between a binary
response variable and predictors, the fit of the model should be assessed. The nature of any …

Background When outcomes are binary, the c-statistic (equivalent to the area under the
Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of …

Calibration, that is, whether observed outcomes agree with predicted risks, is important
when evaluating risk prediction models. For dichotomous outcomes, several tools exist to …

We examine goodnessoffit tests for the proportional odds logistic regression model—the
most commonly used regression model for an ordinal response variable. We derive a test …

The accuracy of a mathematical predictive model is the degree of agreement between
predicted values and the observed outcomes. For dichotomous outcomes, predictions are …