From: Three myths about risk thresholds for prediction models

AUC | Area under the curve, in this case the receiver operating characteristic curve. A measure of discrimination. For prediction models based on logistic regression, this corresponds to the probability that a randomly selected diseased patient had a higher risk prediction than a randomly selected patient who does not have the disease. |

Calibration | Correspondence between predicted and observed risks usually assessed in calibration plots or by calibration intercepts and slopes. |

Sensitivity | The proportion of true positives in truly diseased patients. |

Specificity | The proportion of true negatives in truly non-diseased patients. |

Positive predictive value | The proportion of true positives in patients classified as positive. |

Negative predictive value | The proportion of true negatives in patients classified as negative. |

Decision curve analysis | A method to evaluate classifications for a range of possible thresholds, reflecting different costs of false positives and benefits of true positives. |

Net reclassification improvement | Net reclassification improvement, reflecting reclassifications in the right direction when making decisions based on one prediction model compared to another. |

STRATOS | STRengthening Analytical Thinking for Observational Studies |