Utility Procedure
RECORDS—Prints observations from the input data set, obtains the contents of the input data set, and converts an input data set from one type to another. You can use the SUBPOPN statement to create a subset of a given data set, and you can use the new SORTBY statement to sort your data. RECORDS is a non-analytic procedure.
Descriptive Procedures
CROSSTAB—Computes frequencies, percentage distributions, odds ratios, relative risks, and their standard errors (or confidence intervals) for user-specified cross-tabulations, as well as chi-square tests of independence and the Cochran-Mantel-Haenszel chi-square test for stratified two-way tables.
RATIO—Computes estimates, standard errors and confidence limits of generalized
ratios of the form 
, where 
and 
are observed variables and 
is the weight
variable; also computes standardized estimates and tests single-degree-of-freedom
contrasts among levels of a categorical variable.
DESCRIPT—Computes estimates of means, totals, proportions, percentages, geometric means, quantiles, and their standard errors and confidence limits; also computes standardized estimates and tests of single degree-of-freedom contrasts among levels of a categorical variable.
Regression Procedures
REGRESS—Fits linear regression models and performs hypothesis tests concerning the model parameters. Uses Generalized Estimating Equations (GEE) to efficiently estimate regression parameters with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals.
LOGISTIC—Fits logistic regression models to binary data and computes hypothesis tests for model parameters; also estimates odds ratios and their confidence intervals for each model parameter; uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals.
MULTILOG—Fits logistic and multinomial logistic regression models to ordinal and nominal categorical data and computes hypothesis tests for model parameters; estimates odds ratios and their confidence intervals for each model parameter; uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals.
LOGLINK—Fits log-linear regression models to count data not in the form of proportions. Typical examples involve counts of events in a Poisson-like process where the upper limit to the number is infinite. Uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals.
Survival Procedures
SURVIVAL—Fits discrete and continuous proportional hazards models to failure time data; also estimates hazard ratios and their confidence intervals for each model parameter. Includes facilities for time-dependent covariates, the counting process style of input, stratified baseline hazards, and Schoenfeld and Martingale residuals. Estimates conditional and predicted marginals and tests hypotheses about the marginals.
KAPMEIER— Fits the Kaplan-Meier model, also known as the product limit estimator, to survival data from sample surveys and other clustered data applications. KAPMEIER uses either discrete or continuous time variable to provide point estimates for the survival curve for failure time outcomes that may contain censored observations.