# New Features of SUDAAN 11

SUDAAN 11 continues our tradition of introducing new enhancements that are not available yet in other statistical software packages.We decide on which enhancements to include in each new release based on our customers' needs, requests and feedback.**SUDAAN 11 is 50% bigger than our previous release and offers various enhancements based on customer requests.**New features and enhancements in SUDAAN 11 are summarized below.

# NEW PROCEDURES

*VARGEN Procedure*

*VARGEN Procedure*

VARGEN is a new descriptive statistics procedure introduced in *Release 11*. This procedure computes point estimates and their associated design-based variances for user-defined parameters that can be expressed as complex functions of estimated means, totals, ratios, percents, population variances, population standard deviations, and correlations. Examples include estimating differences between two variables; estimating the population covariance and Pearson correlation between two variables; testing the significance of a mean (or any statistic) against a nonzero value; and estimating a ratio of means or a ratio of ratios. Point estimates can be computed within subgroups, and subgroup contrasts can also be estimated in similar fashion to other descriptive procedures in SUDAAN.

*WTADJX Procedure*

*WTADJX Procedure*

WTADJX is very similar to the WTADJUST procedure introduced in *Release 10*. As with WTADJUST, WTADJX is designed to produce weight adjustments that compensate for unit (*i.e*., whole-record) nonresponse and coverage errors due to undercoverage or duplications in the frame. The primary difference between WTADJUST and WTADJX is that in WTADJUST, the vector of model explanatory variables and the vector of calibration variables must be the same. In WTADJX, the two vectors are allowed to differ. Among other things, this allows researchers to assess the potential for bias in estimates when nonrespondents are not missing at random.

*IMPUTE Procedure*

*IMPUTE Procedure*

IMPUTE is the new item imputation procedure in *Release 11* and replaces the HOTDECK procedure introduced in *Release 10*. IMPUTE extends the capabilities of the previous HOTDECK procedure by including four methods of item imputation: the Cox-Iannacchione Weighted Sequential Hot Deck, cell mean imputation, linear regression imputation for continuous variables and logistic regression imputation for binary variables.

# NEW STATEMENTS FOR EVERY SUDAAN PROCEDURE

*NEWVAR Statement*

*NEWVAR Statement*

The NEWVAR statement allows users to recode existing variables, store the recoded variable in a new variable, and use the new variable in the same procedure for processing (e.g., on a CLASS, MODEL, VAR, or TABLES statement). The NEWVAR statement is available in all procedures and more than one NEWVAR statement can be included in the same procedure call. NEWVAR can create new variables via direct assignment or using IF-THEN-ELSE logic.

*SUBPOPX Statement*

*SUBPOPX Statement*

The new SUBPOPX statement in *Release 11* is used to define a subpopulation in a more flexible way than SUBPOPN.

*BY (RBY) Statement*

*BY (RBY) Statement*

The BY statement (RBY in SAS-Callable SUDAAN) allows users to request output by the values of the variables specified on the BY statement. The new BY statement in *Release 11* is very similar to the BY statement in SAS.

# CROSSTAB PROCEDURE ENHANCEMENTS

*Cohen’s Kappa Measure of Inter-Rater Agreement*

*Cohen’s Kappa Measure of Inter-Rater Agreement*

The new AGREE statement in CROSSTAB allows one to estimate the kappa measure of agreement in square tables. ** Cohen's κ (kappa) Coefficient** is a statistical measure of inter-rater reliability. It is generally thought to be a more robust measure than a simple percent agreement calculation, since

*κ*takes into account the agreement occurring by chance

*Breslow-Day Test for Homogeneity of Odds Ratios in Stratified 2x2 Tables*

*Breslow-Day Test for Homogeneity of Odds Ratios in Stratified 2x2 Tables*

The new BDTEST statement in CROSSTAB provides the Breslow-Day Test for homogeneity of odds ratios in stratified 2x2 tables.

# REGRESS, LOGISTIC, MULTILOG, AND LOGLINK PROCEDURE ENHANCEMENTS

*Confidence Intervals for Predicted and Conditional Marginals*

*Confidence Intervals for Predicted and Conditional Marginals*

Beginning in *Release 11*, all modeling procedures produce 100(1-α)% confidence limits for predicted and conditional marginals, in addition to standard errors and associated *t*-tests.

# LOGISTIC, MULTILOG, LOGLINK, AND SURVIVAL PROCEDURE ENHANCEMENTS

*Odds Ratios, Incidence Density Ratios, and Hazard Ratios for Multiple Unit Change in a Continuous Variable*

*Odds Ratios, Incidence Density Ratios, and Hazard Ratios for Multiple Unit Change in a Continuous Variable*

The LOGISTIC, MULTILOG, LOGLINK, and SURVIVAL procedures will now exponentiate regression coefficients to estimate odds ratios, incidence density ratios, and hazard ratios associated with any multiple unit change in a specified continuous covariate. Previous to this, user-specified odds ratios were only available for a 1-unit change in any covariate.

# ENHANCEMENT TO ALL ITERATIVE MODELING AND WEIGHTING PROCEDURES

*Model Parameter Estimates Available at each Iteration*

*Model Parameter Estimates Available at each Iteration*

For all modeling and weighting procedures (except REGRESS) the model parameters at each iteration of the Newton-Raphson algorithm used to estimate model parameters can be printed or output to a data file using the new output group ITBETAS or keyword ITBETA. This feature is provided to help researchers detect problematic variables that may cause the iterative algorithms to not converge.

# ENHANCEMENTS TO WEIGHT ADJUSTMENTS IN LOGISTIC, WTADJUST, and WTADJX PROCEDURES

*Descriptive Statistics for Weight Adjustment and Response Propensity*

*Descriptive Statistics for Weight Adjustment and Response Propensity*

The LOGISTIC, WTADJUST, and WTADJX procedures will now produce descriptive statistics for the model-predicted response propensity and the weight adjustment. Descriptive statistics that can be obtained include the mean, population variance, population standard deviation, and relative standard deviation of the response propensity and associated weight adjustment. These new statistics can be obtained using the new PREDSTAT statement in SUDAAN. Standard errors associated with these estimates can also be obtained.

*Weighted Response Rates*

*Weighted Response Rates*

The new PREDSTAT statement can also be used to obtain estimates of the ** weighted response rate** and the

**(Representativity Indicator) statistic. The**

*R-indicator**R*-indicator provides a measure of the representativity of the respondents with respect to the sample or population from which they were drawn.

*Precision Estimates That Properly Account for the Estimated Weight Adjustment*

*Precision Estimates That Properly Account for the Estimated Weight Adjustment*

Beginning in *Release 11*, LOGISTIC, WTADJUST, and WTADJX can now properly account for the sample weight adjustment when estimating descriptive statistics (means, totals, percents, ratios) and their standard errors for any user-supplied variable. For each respondent record on the input file, the adjusted sample weight used in the computations is the product of the base weight (supplied on the WEIGHT statement) and the adjustment factor computed in the procedure. New statements associated with the weight-adjusted descriptive statistics include the VAR, NUMER, DENOM, TABLES, VCONTRAST, VDIFFVAR, VPAIRWISE, and VPOLYNOMIAL statements.

*Additional Design Effects in LOGISTIC, WTADJUST, and WTADJX to Measure Impact of Weight Adjustments*

*Additional Design Effects in LOGISTIC, WTADJUST, and WTADJX to Measure Impact of Weight Adjustments*

In addition to providing standard error estimates that account for the weight adjustment, LOGISTIC, WTADJUST, and WTADJX also provide two sets of design effect-like statistics. In other words, the realized gains in statistical efficiency (decreases in standard error) from using one of SUDAAN’s calibration-weighting procedures can now be measured:

1. One set of design effects measures the potential bias from ignoring the estimation of the weight adjustment. These design effects are called MDEFF statistics in *Release 11* and are defined as the variance of an estimate that accounts for the estimation of the nonresponse adjustment relative to the variance of an estimate that ignores this estimation. The variance estimates in both the numerator and denominator of the MDEFF statistics also account for the complex sample design.

2. The second set of design effects provides a measure of the effect of the weight adjustment on the variance of estimated means, percents, ratios, and totals. These are referred to as the ADEFF statistics. The numerator of the ADEFF design effect is a variance estimate that properly accounts for the estimation of the weight adjustment from the model in LOGISTIC, WTADJUST, and WTADJX. The denominators of these design effects are variance estimates that assume the weight adjustment is equal to 1.00. The variance estimates in both the numerator and denominator account for the complex sample design.

# WTADJUST PROCEDURE ENHANCEMENT

*Additional Summary Statistics in WTADJUST*

*Additional Summary Statistics in WTADJUST*

Several additional summary statistics have been added to the WTADJUST procedure.

Additional Information on the new features and capabilities found in *Release 11* can be found here.