# What is time-dependent ROC curve?

## What is time-dependent ROC curve?

Thus, time-dependent ROC curve is an efficient tool in measuring the performance of a candidate marker given the true disease status of individuals at certain time points. In longitudinal studies, the marker is measured several times within a fixed follow-up.

## What is time-dependent AUC?

By definition, time-dependent AUC curves quantify the discriminative ability of a marker at each time point under consideration.

When would you use a ROC curve?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.

What is ROCS medical abbreviation?

Acronym for receiver operating characteristic, an analytic expression of diagnostic accuracy.

### Is C statistic the same as AUC?

We let AUC denote the area under the ROC curve, which is equivalent to the c-statistic.

### What is a concordance index?

The concordance index or c-index is a metric to evaluate the predictions made by an algorithm. It is defined as the proportion of concordant pairs divided by the total number of possible evaluation pairs. Let’s see through some examples what does this definition mean in practice.

What is threshold in ROC curve?

The false-positive rate is plotted on the x-axis and the true positive rate is plotted on the y-axis and the plot is referred to as the Receiver Operating Characteristic curve, or ROC curve. This would be a threshold on the curve that is closest to the top-left of the plot.

What is ROC curve threshold?

The ROC curve is produced by calculating and plotting the true positive rate against the false positive rate for a single classifier at a variety of thresholds. For example, in logistic regression, the threshold would be the predicted probability of an observation belonging to the positive class.

#### How do you choose threshold on ROC curve?

A really easy way to pick a threshold is to take the median predicted values of the positive cases for a test set. This becomes your threshold. The threshold comes relatively close to the same threshold you would get by using the roc curve where true positive rate(tpr) and 1 – false positive rate(fpr) overlap.

#### When to use a time dependent ROC curve?

Time-dependent ROC curves for censored survival data and a diagnostic marker ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D.

Are there ROC curves for censored survival data?

A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c.

How is ROC used in survival prediction models?

Time-dependent ROC for Survival Prediction Models in R. Use of receiver operator curves (ROC) for binary outcome logistic regression is well known. However, the outcome of interest in epidemiological studies are often time-to-event outcomes.

## When to use ROC in dynamic control models?

The incident case/dynamic control ROC may be of use for examining how long the time-zero marker remains relevant in predicting later events. Heagerty, Patrick J. and Zheng, Yingye, Survival Model Predictive Accuracy and ROC Curves, Biometrics, 61 (1), pp. 92-105 (2005). doi:10.1111/j.0006-341X.2005.030814.x.