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Statistics
ROC curve analysis
File:
Example file - Bland and Altman plot.mc1
Example file - Cox regression - Gallstones.mc1
Example file - Logistic regression.mc1
Example file - McNemar test.mc1
Example file - Meta-analysis - Continuous measure.mc1
Example file - Meta-analysis - Correlation.mc1
Example file - Meta-analysis - Generic.mc1
Example file - Meta-analysis - Odds ratio.mc1
Example file - Meta-analysis - Proportions.mc1
Example file - Meta-analysis - Risk ratio & difference.mc1
Example file - Meta-analysis - ROC area.mc1
Example file - Nonlinear regression.mc1
Example file - Patients data.mc1
Example file - Probit (grouped data).mc1
Example file - Relative risk.mc1
Example file - ROC curve analysis.mc1
Example file - Survival.mc1
ROC curve analysis
Variables
Variable:
MINI_WRIGHT1
MINI_WRIGHT2
true
WRIGHT1
WRIGHT2
Select a variable.
Classification:
MINI_WRIGHT1
MINI_WRIGHT2
true
WRIGHT1
WRIGHT2
Select a variable.
Filter:
MINI_WRIGHT2
Methodology
Standard Error of AUC:
DeLong et al.
Hanley & McNeil
Binomial exact Confidence Interval for AUC
Disease prevalence
Equal to ratio of positive and negative cases
or, equal to:
Enter a value.
Options
List criterion values with test characteristics
Include:
Sensitivity/specificity
95% CI
Likelihood ratios
95% CI
Predictive values
95% CI
Calculate optimal criterion value taking into account costs:
FPc:
Enter a value.
FNc:
Enter a value.
TPc:
Enter a value.
TNc:
Enter a value.
Graph
Create graph
Mark points corresponding to criterion values
Include 95% Confidence Bounds
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