Not exact matches
The principal measure of efficacy is called
Area Under the
Curve (AUC), a measure of how
well biomarkers identify true cases of disease (sensitivity) while avoiding false positives (specificity).
CSF leucocytosis differentiated
best between bacterial meningitis and other diagnoses (
area under the
curve [AUC] 0.95) or any neurological infection versus other diagnoses (AUC 0.93).
However, samtools yielded
better results, translating into a larger
area under curve (AUC) in the receiver operating characteristic (ROC)
curves comparing with the Affymetrix SNP6 array.
The
area under the
curve (AUC) is a
good indicator of your IR.
To determine which combination of measures
best predicted outcome, we tested the discrimination, or performance, of each model by calculating the
area under the
curve (AUC), which quantified each model's ability to classify a dog correctly as an eventual program release or success (higher AUCs indicate
better predictive power)(54, 55)(SI Materials and Methods).
Receiver operating characteristic
curves and the
area under the
curves (AUC) for the Composite International Diagnostic Interview Short - Form (CIDI - SF), the K10 and K6 screening scales, and the World Health Organization Disability Assessment Schedule (WHO - DAS) as
well as for illustrative multivariate prediction equations.
To identify which scales
best discriminate specific comorbidities, we examined the
area under the
curve (AUC) using receiver operating characteristic (ROC) analysis as described above (see table 2).