The GEE Procedure (Experimental)

REPEATED Statement

• REPEATED SUBJECT=subject-effect </ options>;

The REPEATED statement specifies the correlation structure of the responses for GEE model fitting. In addition, the REPEATED statement controls the iterative fitting algorithm and specifies optional output.

Table 42.6 summarizes the options available in the REPEATED statement.

Table 42.6: REPEATED Statement Options

Option

Description

Specifies the convergence criterion for GEE parameter estimation

Displays the estimated correlation matrix

Displays the estimated working correlation matrix

Displays the estimated covariance matrix

Displays the estimated empirical correlation matrix

Displays the estimated empirical covariance matrix

Specifies initial values of the regression parameters estimation

Specifies an initial value of the intercept

Specifies the maximum number of iterations

Displays the estimated model-based correlation matrix

Displays the estimated model-based covariance matrix

Displays a parameter estimates table with the model-based standard errors

Identifies a different subject (cluster)

Specifies the working correlation matrix structure

Specifies the order of measurements within subjects

You must specify the SUBJECT= option:

SUBJECT=subject-effect

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 (level) of the effect identifies a different subject (cluster). Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated. You must specify a subject-effect, and you must list variables that are used in defining the subject-effect in the CLASS statement.

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

CONVERGE=number

specifies the convergence criterion for GEE parameter estimation. If the maximum absolute difference between regression parameter estimates is less than 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.

CORRB

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

CORRW

displays the estimated working correlation matrix. If you specify TYPE=EXCH for the exchangeable working correlation structure, then the CORRW option is not needed to view the estimated correlation, because a table that contains the single estimated correlation is printed by default.

COVB

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

ECORRB

displays the estimated regression parameter empirical correlation matrix.

ECOVB

displays the estimated regression parameter empirical covariance matrix.

INITIAL=numbers

specifies initial values of the regression parameters estimation, other than the intercept parameter, for GEE estimation. If you do not specify this option, then the estimated regression parameters (assuming independence for all responses) are used for the initial values.

INTERCEPT=number

specifies an initial value of the intercept regression parameter in the GEE model.

MAXITER=number
MAXIT=number

specifies the maximum number of iterations allowed in the iterative GEE estimation process. By default, MAXITER=50.

MCORRB

displays the estimated regression parameter model-based correlation matrix.

MCOVB

displays the estimated regression parameter model-based covariance matrix.

MODELSE

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

TYPE=correlation-structure-keyword
CORR=correlation-structure-keyword

specifies the structure of the working correlation matrix that is used to model the correlation of the responses from subjects. You can specify the values that are shown in Table 42.7 (for definitions of the correlation matrix types, see Table 42.8 in the section Details: GEE Procedure):

Table 42.7: Correlation Structure Types

Keyword

Correlation Matrix Type

AR | AR(1)

Autoregressive(1)

EXCH | CS

Exchangeable

IND

Independent

MDEP(number)

m-dependent, where m = number

UNSTR | UN

Unstructured

USER(matrix) | FIXED(matrix)

Fixed, user-specified correlation matrix

By default, TYPE=IND.

For example, the following option specifies a fixed correlation matrix:

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 )

WITHINSUBJECT=within-subject-effect
WITHIN=within-subject-effect

defines an effect that specifies 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 you do not specify the WITHIN= option for the standard GEE method, missing values are assumed to be the last values and are not used; the remaining observations are then ordered in the sequence in which they are provided in the input data set. If you do not specify the WITHIN= option for the weighted GEE method, the observations are assumed to be ordered in the sequence in which they are provided in the input data set.

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