2007 expect when the predicitive value is 0 or 100%, in which case a Clopper-Pearson confidence interval is reported. 2000.Ĭonfidence intervals for the predictive values are the standard logit confidence intervals given by Mercaldo et al. Sensitivity, specificity, disease prevalence, positive and negative predictive value as well as accuracy are expressed as percentages.Ĭonfidence intervals for sensitivity, specificity and accuracy are 'exact' Clopper-Pearson confidence intervals.Ĭonfidence intervals for the likelihood ratios are calculated using the 'Log method' as given on page 109 of Altman et al. $$ Accuracy = sensitivity imes prevalence + specificity imes (1-prevalence) $$

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.

Specificity: probability that a test result will be negative when the disease is not present (true negative rate).

Sensitivity: probability that a test result will be positive when the disease is present (true positive rate).

The following statistics are reported with their 95% Confidence intervals: This will have an effect on the positive and negative predictive values, and accuracy.

If the sample sizes in the positive (Disease present) and the negative (Disease absent) groups do not reflect the real prevalence of the disease, you can enter the disease prevalence in the corresponding input box. Note: you can change the order of columns and rows by clicking the button. Required inputĮnter 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). Allows to calculate test characteristics such as sensitivity, specificity, positive and negative likelihood ratio, disease prevalence as well as positive and negative predictive value, from data entered in a 2x2 table.

Download medcalc 2x2