Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three persona...

BACKGROUND The area under the ROC curve (AUC) is widely used to measure the diagnostic accuracy of noninvasive fibrosis indices. However, use of the AUC assumes a binary gold standard, whereas fibrosis staging is based on an ordinal scale and also depends on the distribution of fibrosis stages in the study sample. We explored other fibrosis staging accuracy measures designed for ordinal gold st...

The aim of the study was to determine the association between the number of natural teeth and oral impacts in Sri Lankan adults. The sample consisted of 476, 40-59 and 452, ≥60 year olds. Oral impacts were assessed using a validated Sinhalese translation of the Oral Health Impact Profile-14 scale. A receiver-operating characteristic (ROC) curve was plotted to determine the number of natural tee...

We study generalization properties of the area under the ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for the bipartite ranking problem. The AUC is a different term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions about the AUC. I...

Performance evaluation of any classification method is fundamental to its acceptance in practice. Evaluation should consider the dependence of a classifier’s accuracy on relevant covariates in addition to its overall accuracy. When developing a classifier with a continuous output that allocates units into one of two groups, receiver operating characteristic (ROC) curve analysis is appropriate. ...

We study generalization properties of the area under an ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for bipartite ranking problems. The AUC is a different and more complex term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions abo...

For a continuous-scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the AUC and show that its limiting distribution is a s...