Phillips Curve Methodology Is Used On An - amazonia.fiocruz.br

Phillips Curve Methodology Is Used On An Video

Amol Agrawal

The area under the receiver operating characteristic ROC curve AUC is commonly used for assessing the discriminative ability of prediction Mehhodology even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions.

We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions.

For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions.

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AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility. Key Messages The receiver operating characteristic ROC plot is an alternative way of presenting the risk distributions of diseased and non-diseased individuals. In the ROC plot, the separation of the risk distributions is indicated by the area between the ROC curve and the diagonal. The more separation between the risk distributions of the diseased and non-diseased individuals, the larger the area between the ROC curve and the diagonal, and the higher the AUC.

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Criticism that the AUC lacks clinical relevance is valid: the AUC is a measure of the discriminative ability of a prediction model, not of clinical utility. InPhiloips Lusted introduced the receiver operating characteristic ROC curve in medicine to contrast the percentage of true-positive against false-positive diagnoses for different decision criteria applied by a radiologist. Despite its popularity, the AUC is frequently criticized and its interpretation has been a challenge since its introduction in medicine.

This probability is considered clinically irrelevant, as doctors never have two random people in their office 34 ; they are only interested in the clinically relevant thresholds of the ROC curve, not in others 5 ; and they often want to distinguish multiple risk categories for which they need more than one threshold. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals. In this paper, we show that the ROC curve is an alternative graphical presentation of these risk distributions.

We explain how the ROC curve gives information about the shapes and overlap of the underlying risk distributions, and re-evaluate the interpretation and purported limitations of the AUC from this alternative perspective.]