Biostat/ stat --> Discrimination / calibrarion

Question

what is the difference between calibration and discrimination in data analysis? an dhow do we measure them

Answer 1

Calibration

Calibration refers to the agreement between the observed outcomes and the predicted probabilities. A well-calibrated model will accurately reflect the true risk of the outcome. For example, if a model predicts a 30% chance of an event, then we would expect to see that event occur approximately 30% of the time in a group of similar cases.

1. How to Measure: Calibration is often assessed using a calibration plot, which compares the predicted probabilities (on the x-axis) against the observed frequencies (on the y-axis). Perfect calibration is indicated by a 45-degree line where the predicted probability matches the observed frequency. Other methods include the Hosmer-Lemeshow test, which divides the dataset into deciles based on predicted risk and compares the number of observed and expected outcomes in each decile.

Discrimination

Discrimination refers to the model's ability to distinguish between those who will experience the event and those who will not. It is the ability of the model to correctly classify cases into different outcome categories.

1. How to Measure: Discrimination is commonly measured using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) for different cutoff points. An AUC of 0.5 indicates no discriminative ability (equivalent to random chance), while an AUC of 1.0 indicates perfect discrimination. Another measure of discrimination is the C-statistic, which is equivalent to the AUC for binary outcomes.