Exact confidence intervals for a proportion.
Select bootstrap samples.
Cumulative incidence in the presence of competing risks.
Competing risk survival analysis with covariates.
Estimates the distance matrix between two groups (e.g. cases and potential controls) on the basis of a set of X’s.
Uses the method of Contal and O’Quigley (1999) to find the best cutpoint in a continuous variable with regards to a survival outcome.
Computerized matching of cases to controls using the greedy matching algorithm
Produce gplot of continuous variable(y-axis) vs a group variable(x-axis) in such a way that no points are hidden.
Univariate logistic regression model summaries with multiple dependent variables and predictors.
Computes Lin’s concordance correlation coefficient (CCC) for any number of raters.
Conducts likelihood ratio tests for nested logistic and Cox proportional hazards models.
Uses Graph Template Language to create a highly customizabile Kaplan-Meier curve.
This macro creates a macro variable containing the number of observations in a SAS dataset.
Creates a single RTF file containing multiple tables created by %SUMMARY.
Proc Plot with correlation/regression statistics appended.
Create a scatterplot matrix graphically displaying the bivariate relationships between a number of variables.
Estimated Integrated Discrimination Index (IDI) and Net Reclassification Improvement (NRI) for comparison of a new risk model to an old model.
Schoenfeld residuals for proportional hazards model.
Creates a table of variable summaries plus test statistics for the difference between two or more independent samples.
Complete Kaplan-Meier survival analysis with printing options and logrank statistic.
Calculates the c-statistic (concordance, discrimination index) for survived data with time dependent covariates
Calculates logrank statatistics for the surv macro.
General survival statistics p(t), standard error, confidence limits, and median survival time, for the left-truncated survival analyses.
Creates high-quality and easily customized Kaplan-Meier plots.
Checks for symmetry and suggests the best power transformation, if one exists, to make an asymmetric distribution symmetric
Measures agreement, precision, accuracy, total deviation index and coverage probability.
Computerized matching of cases to controls using variable optimal matching.