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The GENMOD Procedure 
Explanatory variables in the model are Intercept (x_{ij1}), treatment (x_{ij2}), center (x_{ij3}), sex (x_{ij4}), age (x_{ij6}), and baseline (x_{ij6}), so that I>xp = [x_{ij1},x_{ij2}, ... ,x_{ij6}] is the vector of explanatory variables. Indicator variables for the classification explanatory variables can be automatically generated by listing them in the CLASS statement in PROC GENMOD. However, in order to be consistent with the analysis
in Stokes, Davis, and Koch (1995), the four classification explanatory variables are coded as follows:
Suppose y_{ij} represents the respiratory status of patient i at the jth visit, j = 1, ... ,4, and represents the mean of the respiratory status. Since the response data are binary, you can use the variance function for the binomial distribution and the logit link function .The model for the mean is , where is a vector of regression parameters to be estimated.
Further manipulation of the data set creates an observation for each visit with the respiratory status at each visit represented by the binary variable outcome and indicator variables for treatment (active), center (center2), and sex (female).
data resp; keep id active center center2 female age baseline visit outcome; input center id treatmnt $ sex $ age baseline visit1visit4; active=(treatmnt='A'); center2=(center=2); female=(sex='F'); visit=1; outcome=visit1; output; visit=2; outcome=visit2; output; visit=3; outcome=visit3; output; visit=4; outcome=visit4; output; datalines; 1 1 P M 46 0 0 0 0 0 1 2 P M 28 0 0 0 0 0 1 3 A M 23 1 1 1 1 1 1 4 P M 44 1 1 1 1 0 1 5 P F 13 1 1 1 1 1 . . . 1 52 P M 43 0 0 0 1 0 1 53 A F 32 0 0 0 1 0 1 54 A M 11 1 1 1 1 0 1 55 P M 24 1 1 1 1 1 1 56 A M 25 0 1 1 0 1 2 1 P F 39 0 0 0 0 0 2 2 A M 25 0 0 1 1 1 2 3 A M 58 1 1 1 1 1 2 4 P F 51 1 1 0 1 1 2 5 P F 32 1 0 0 1 1
. . . 2 51 A M 43 1 1 1 1 0 2 52 A F 39 0 1 1 1 1 2 53 A M 68 0 1 1 1 1 2 54 A F 63 1 1 1 1 1 2 55 A M 31 1 1 1 1 1 ;The GEE solution is requested with the REPEATED statement in the GENMOD procedure. The option SUBJECT=ID(CENTER) specifies that the observations in a single cluster are uniquely identified by center and id within center. The option TYPE=UNSTR specifies the unstructured working correlation structure. The MODEL statement specifies the regression model for the mean with the binomial distribution variance function.
proc genmod data=resp; class id center; model outcome=center2 active female age baseline / d=bin; repeated subject=id(center) / type=unstr corrw; run;
These statements first produce the usual output (not shown) for fitting the generalized linear (GLM) model specified in the MODEL statement. The parameter estimates from the GLM model are used as initial values for the GEE solution.
Information about the GEE model is displayed in Output 29.5.1. The results of GEE model fitting are displayed in Output 29.5.2. If you specify no other options, the standard errors, confidence intervals, Z scores, and pvalues are based on empirical standard error estimates. You can specify the MODELSE option in the REPEATED statement to create a table based on modelbased standard error estimates.
Output 29.5.1: Model Fitting Information


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