Calculator 1. Given a sample of subjects cross-classified according to whether a certain condition is present or absent, and according to whether a test designed to indicate the presence of that condition proves positive or negative, this unit will calculate the estimated population midpoints and 95% confidence intervals for prevalence, sensitivity, specificity, predictive values (probabilities for true positive, true negative, false positive, and false negative), and likelihood ratios.
Calculator 2. Given the prevalence of a condition within the population and the sensitivity and specificity of a test designed to indicate the presence of that condition, this unit will calculate the predictive values of the test (probabilities for true positive, true negative, false positive, and false negative) and its positive and negative likelihood ratios.
Calculator 3. For two groups of subjects, each sorted according to the absence or presence of some particular characteristic or condition, this unit will calculate standard measures for Rates, Risk Ratio, Odds, Odds Ratio, and Log Odds. It will also calculate the Phi coefficient of association and perform a chi-square test of association (for large samples) or a Fisher exact probability test (for small samples).
Kaplan-Meier Survival Probability Estimates. Estimates time-defined survival probabilities in cases where the data might be incomplete owing to the fact that some subjects become unavailable at various points during the term of the study.
McNemar's Test for Correlated Proportions in the Marginals of a 2x2 Contingency Table. Assesses the significance of the difference between two correlated proportions, such as might be found in the case where the two proportions are based on the same sample of subjects or on matched-pair samples.
Chi-Square, Cramer's V, and Lambda for a rows by columns contingency table containing up to 5 rows and 5 columns.
Kappa as a Measure of Concordance in Categorical Sorting. Calculates unweighted kappa and kappa with linear and quadratic weightings, along with some other measures of concordance.
Simple Logistic Regression. [The plain-vanilla version.]
Simple ROC Curve Analysis. The programming for this unit provides a streamlined approach to ROC curve analysis that I think will be fairly accessible to the non-statistician. For the more heavy-duty version of this procedure, applicable software can be downloaded from the Department of Radiology, Kurt Rossmann Laboratories, University of Chicago.
Significance of the Difference between the Areas under Two Independent ROC Curves. For two ROC curves derived from independent samples, this calculator will assess the significance of the difference between the areas that lie under the curves.
Estimation of a Sample’s Mean and Variance from Its Median and Range. This program estimates the mean, variance, and standard deviation of a sample on the basis of the sample’s reported median and range according to the method devised by S.P. Hozo, B. Djulbegovic, and I. Hozo.