Allows to calculate test characteristics such as sensitivity, specificity, positive and negative likelihood ratio, disease prevalence as well as positive and negative predictive power, from a 2x2 table.
Enter the number of cases in the diseased group that test
positive and negative (left column); and the number of cases in the non-diseased group that test
positive and negative (right column).
- Sensitivity: probability that a test result will be positive when the disease is present (true positive rate).
- Specificity: probability that a test result will be negative when the disease is not present (true negative rate).
- Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the disease and the probability of a positive test result given the absence of the disease, i.e.
= True positive rate / False positive rate = Sensitivity / (1-Specificity)
- Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the disease and the probability of a negative test result given the absence of the disease, i.e.
= False negative rate / True negative rate = (1-Sensitivity) / Specificity
- Positive predictive value: probability that the disease is present when the test is positive.
- Negative predictive value: probability that the disease is not present when the test is negative.
Sensitivity, specificity, positive and negative predictive value as well as disease prevalence are expressed as percentages for ease of interpretation.
In the Comment input field you can enter a comment or conclusion that will be included on the printed report.
If the sample sizes in the positive (Disease present) and the negative (Disease absent) groups do not reflect the real prevalence of the disease,
then the Positive and Negative predicted values cannot be estimated and you should ignore those values.
Alternatively, when the disease prevalence is known then the positive and negative predictive values
can be calculated using the following formula's based on Bayes' theorem:
|sensitivity x prevalence|
|sensitivity x prevalence + (1-specificity) x (1-prevalence)|
|specificity x (1-prevalence)|
|(1-sensitivity) x prevalence + specificity x (1-prevalence)|
- Gardner IA, Greiner M (2006) Receiver-operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests. Veterinary Clinical Pathology, 35:8-17.
- Griner PF, Mayewski RJ, Mushlin AI, Greenland P (1981) Selection and interpretation of diagnostic tests and procedures. Annals of Internal Medicine, 94, 555-600.
- Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36.
- Metz CE (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8, 283-298.
- Zhou XH, NA Obuchowski, DK McClish (2002) Statistical methods in diagnostic medicine. New York: Wiley.
- Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39, 561-577.