![]() Altman DG, Machin D, Bryant TN, Gardner MJ (Eds) (2000) Statistics with confidence, 2 nd ed.2007 except 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. ROC curves are compared based on calculating the difference between the areas under the curves using standard error, 95% confidence interval and P-value.$$ Accuracy = Sensitivity \times Prevalence + Specificity \times (1 - Prevalence) $$ You can choose threshold values in the dot diagram and the corresponding specificity and sensitivity is automatically calculated. The area under the curve is calculated with a 95% confidence interval and standard error. MedCalc provides a complete on line help documentation accessible on the official website but also an extensive PDF manual along with the application, for offline use. Missing data is handled appropriately and import is supported for various file formats like Excel, Dbase, Lotus, SPSS, DIF, SYLK and text. ![]() This highly useful piece of software comes with a built-in spreadsheet that can include as many as 10,000 rows. If you work in the biomedical field and often need to generate statistics from tons of data you’ll probably enjoy working with MedCalc. MedCalc: Powerful statistical application for biomedical sciences
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