About MedCalc ROC analysis
most user-friendly software for Receiver Operating Characteristic curve (ROC curves) analysis. The MedCalc ROC module includes comparison of up to 6 ROC curves.
most user-friendly software for Receiver Operating Characteristic curve (ROC curves) analysis. The MedCalc ROC module includes comparison of up to 6 ROC curves.
MedCalc allows to perform ROC curve analysis easily and accurately. Our ROC curve analysis module includes:
Area under the curve (AUC) with standard error (SE) and 95% confidence interval (CI). The standard error can be calculated using 2 methods:
DeLong et al. (1988)
Hanley & McNeil (1982)
The Confidence Interval for the AUC can be calculated using the following methods:
Binomial exact confidence interval
AUC ± 1.96 its Standard Error.
Calculate optimal criterion value taking into account costs: option to calculate the optimal criterion value taking into account the disease prevalence and cost of false and true positive and negative decisions (Zweig & Campbell, 1993).
Calculation of Youden index.
ROC curve graph with 95% Confidence Bounds.
Plot of sensitivity and specificity versus criterion values.
Interval likelihood ratios.
Sample size calculation for area under ROC curve and comparison of ROC curves.
MedCalc creates a list of sensitivity, specificity, likelihood ratios, and positive and negative predictive values for all possible threshold values.
Threshold values can be selected in an interactive dot diagram with automatic calculation of corresponding sensitivity and specificity.
Previous Versions
Here you can find the changelog of MedCalc ROC analysis since it was posted on our website on 2015-01-26 09:35:05.
The latest version is 2015 and it was updated on 2024-03-28 21:45:34. See below the changes in each version.
MedCalc ROC analysis version 2015
Updated At: 2014-12-31
Disclaimer
External Download
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