SUDAAN is a single program comprising a family of ten analytic and three new pre-analytic procedures. The three pre-analytic procedures include two that compute weight adjustments using a model-based, weight calibration methodology (WTADJUST, WTADJX) and a third procedure that performs the weighted sequential hot deck, cell mean, and regression-based (linear and logistic) methods of imputation for item nonresponse (IMPUTE). SUDAAN procedures are used to analyze data from complex sample surveys and other observational and experimental studies involving repeated measures and cluster-correlated data. Included in SUDAAN are procedures for descriptive statistics and regression modeling.

### Weighting and Imputation Procedures

**WTADJUST**— Produces nonresponse and post-stratification sample weight adjustments using a model-based, calibration approach. A weight
truncation option is available that can be used to trim extreme weights. Any loss/gain in the weight sum is accounted for in the subsequent
computation of the weight adjustments.

**WTADJX** —** New in Release 11**: As in WTADJUST, WTADJX produces
nonresponse and post-stratification sample weight adjustments using a model-based, calibration approach. WTADJX, however, allows
the user to specify a set of calibration variables used to estimate model parameters that vary from the model explanatory variables. Among
other things, this means survey items known only for respondents can be used as explanatory variables in the weight adjustment model.

**IMPUTE**— Performs the weighted sequential hot deck and, **new**** in Release 11**,
cell mean, and regression-based (linear and logistic) methods of imputation for item nonresponse.

### 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 a series of Cochran-Mantel-Haenszel chi-square
tests associated with stratified two-way tables. ** Release 11** adds statistics
related to the Kappa measure of agreement in square tables and the Breslow-Day test for homogeneity of odds ratios in stratified 2x2 tables.

**RATIO**—Computes estimates, standard errors, and confidence limits of generalized ratios of the
form Σ_{i} w_{i}x_{i } / Σ_{i} w_{i}y_{i}. 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.

**VARGEN—New in Release 11**: Computes point estimates, design-based
variances, and contrast estimates for any user-defined parameter that can be expressed as a function of means, totals, proportions, ratios, population
variances, population standard deviations, and correlations. This means that VARGEN, for example, can estimate a ratio as well as a ratio of ratios.

### 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. Estimates exponentiated contrasts among model parameters (with confidence intervals). 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. ** Release 11** adds
hazard ratios for a multiple-unit increase or decrease in a model covariate.

**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 (Section 23).

### 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. ** Release 11** adds confidence intervals for 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; estimates exponentiated contrasts among model parameters (with confidence intervals), 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. ** Release 11** adds confidence intervals for
marginals, as well as odds ratios for a multiple-unit increase or decrease in a model covariate.

**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; estimates exponentiated contrasts among
model parameters (with confidence intervals), 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. ** Release 11** adds
confidence intervals for marginal, as well as odds ratios for a multiple-unit increase or decrease in a model covariate.

**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. Estimates incidence density ratios and confidence intervals for
each model parameter. Estimates exponentiated contrasts among model parameters (with confidence intervals). 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. ** Release 11** adds confidence intervals for marginals, as
well as incidence density ratios for a multiple-unit increase or decrease in a model covariate.

### Utility Procedure

**RECORDS**—Prints observations from the input data set, obtains the contents of the input data set, converts an input data set from one type
to another. You can use the SUBPOPN or SUBPOPX statement to create a subset of a given data set, and you can use the SORTBY statement
to sort your data. RECORDS is a non-analytic procedure.