Statistical software for biomedical research with a rich set of functions, graph types and an advanced module for performing ROC graph analysis. MedCalc is designed to meet the requirements of biomedical researchers with respect to the statistical analysis of large datasets. It provides the necessary tools and features for performing Receiver Operating Characteristic curve analysis, data plotting, Bablok and Deming regression and more. With an integrated spreadsheet with over 100,000 rows, MedCalc is capable of reading and displaying detailed data imported from Excel, SPSS, Dbase, Lotus or extracted from SYLK, DIF or text files. The information can be easily sorted, filtered or edited.

The built-in data browser offers a comfortable means of easily managing data, variables, notes, texts and graphs, while the array of supported graphs and diagrams (scatter plots, method comparison graphs, graphs for subgroups or for up to 24 continuous variables, survival curves, serial measurement, standardized mean plots and many more) make it perfect for analyzing trends and comparing information.

One of the most important features of MedCalc is related to its ROC curve analysis capabilities. It can generate the ROC curve graph with 95% confidence bounds, calculate specificity, sensitivity, predictive values for all the thresholds, likelihood ratios, generate conclusive plots and determine the size of an area under the ROC graph. Up to 6 ROC curves can be compared, calculating the differences between the areas, the standard errors, P-values and more.

MedCalc is capable of handling missing data, creating subgroups, calculating percentile ranks and power transformation. It features outlier detection, correlation and regression tools, Bland & Altman plotting, while also enabling you to run Anova, variance ratio, mean, propertion, Chi-Square, Fisher and T-tests.
A summary of the statistical report can be easily generated and data can be placed and viewed side-by-side thanks to the multiple comparison graphs function.
MedCalc requires at least basic statistics knowledge in order to get the most out of its potential. Its extensive array of features make it a must-have tool for running method comparison studies and analyzing biomedical data.

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