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. = Sensitivity × Prevalence + Specificity × (1 − Prevalence) Accuracy: overall probability that a patient is correctly classified.Negative predictive value: probability that the disease is not present when the test is negative.Positive predictive value: probability that the disease is present when the test is positive.

= False negative rate / True negative rate = (1-Sensitivity) / 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.= True positive rate / False positive rate = Sensitivity / (1-Specificity) 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).(*) These values are dependent on disease prevalence.

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