identifies subjects in the input data set. The subject-effect can be a single variable, an interaction effect, a nested effect, or a combination. Each distinct value, or level, of the effect identifies a different subject, or cluster. Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated. A subject-effect must be specified, and variables used in defining the subject-effect must be listed in the CLASS statement. The input data set does not need to be sorted by subject (see the SORTED option).

The options control how the model is fit and what output is produced. You can specify the following options after a slash (/).

specifies initial values for log odds ratio regression parameters if the LOGOR= option is specified for binary data. If this option is not specified, an initial value of 0.01 is used for all the parameters.

specifies the convergence criterion for GEE parameter estimation. If the maximum absolute difference between regression parameter estimates is less than the value of number on two successive iterations, convergence is declared. If the absolute value of a regression parameter estimate is greater than 0.08, then the absolute difference normalized by the regression parameter value is used instead of the absolute difference. The default value of number is 0.0001.

displays the estimated working correlation matrix. If you specify an exchangeable working correlation structure with the CORR=EXCH option, the CORRW option is not needed to view the estimated correlation, since a table is printed by default that contains the single estimated correlation.

displays the estimated regression parameter correlation matrix. Both model-based and empirical correlations are displayed.

displays the estimated regression parameter covariance matrix. Both model-based and empirical covariances are displayed.

displays the estimated regression parameter empirical correlation matrix.

displays the estimated regression parameter empirical covariance matrix.

specifies either an initial or a fixed value of the intercept regression parameter in the GEE model. If you specify the NOINT option in the MODEL statement, then the intercept is fixed at the value of number.

specifies initial values of the regression parameters estimation, other than the intercept parameter, for GEE estimation. If this option is not specified, the estimated regression parameters assuming independence for all responses are used for the initial values.

specifies the regression structure of the log odds ratio used to model the association of the responses from subjects for binary data. The response syntax must be of the single variable type, the distribution must be binomial, and the data must be binary. Table 37.5 displays the log odds ratio structure keywords and the corresponding log odds ratio regression structures. See the section Alternating Logistic Regressions for definitions of the log odds ratio types and examples of specifying log odds ratio models. You should specify either the LOGOR= or the TYPE= option, but not both.

specifies the maximum number of iterations allowed in the iterative GEE estimation process. The default number is 50.

displays the estimated regression parameter model-based correlation matrix.

displays the estimated regression parameter model-based covariance matrix.

displays an analysis of parameter estimates table that uses model-based standard errors for inference. By default, an "Analysis of Parameter Estimates" table based on empirical standard errors is displayed.

displays an analysis of maximum likelihood parameter estimates table. The maximum likelihood estimates are not displayed unless this option is specified.

specifies the number of iterations between updates of the working correlation matrix. For example, RUPDATE=5 specifies that the working correlation is updated once for every five regression parameter updates. The default value of number is 1; that is, the working correlation is updated every time the regression parameters are updated.

specifies that the input data are grouped by subject and sorted within subject. If this option is not specified, then the procedure internally sorts by subject-effect and within subject-effect, if a within subject-effect is specified.

specifies a variable defining subclusters for the 1-nested or k-nested log odds ratio association modeling structures. This variable must be listed in the CLASS statement.

specifies the structure of the working correlation matrix used to model the correlation of the responses from subjects. Table 37.6 displays the correlation structure keywords and the corresponding correlation structures. The default working correlation type is the independent (CORR=IND). See the section Details: GENMOD Procedure for definitions of the correlation matrix types. You should specify LOGOR= or TYPE= but not both.

For example, you can specify a fixed correlation matrix with the following option:

TYPE=USER( 1.0  0.9  0.8  0.6
           0.9  1.0  0.9  0.8
           0.8  0.9  1.0  0.9
           0.6  0.8  0.9  1.0 )

specifies that the SAS ‘Version 6’ method of computing the normalized Pearson chi-square be used for working correlation estimation and for model-based covariance matrix scale factor.

defines an effect specifying the order of measurements within subjects. Each distinct level of the within subject-effect defines a different response from the same subject. If the data are in proper order within each subject, you do not need to specify this option.

If some measurements do not appear in the data for some subjects, this option properly orders the existing measurements and treats the omitted measurements as missing values. If the WITHINSUBJECT= option is not used in this situation, measurements might be improperly ordered and missing values assumed for the last measurements in a cluster.

Variables used in defining the within subject-effect must be listed in the CLASS statement.

specifies the variables in the ZDATA= data set corresponding to pairs of responses for log odds ratio association modeling.

specifies a SAS data set containing either the full z matrix for log odds ratio association modeling or the z matrix for a single complete cluster to be replicated for all clusters.

specifies the variables in the ZDATA= data set corresponding to rows of the z matrix for log odds ratio association modeling.