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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