Hi, I am fitting the following model using GEE:

proc genmod data=mydata; class id tx; model y = tx|week blY /dist=normal type3; repeated subject=id(tx)/type=ar(1) covb corrw; output out=genout pred=preds reschi=resid; run;

Y is a continuous outcome between 0 and 30 (upper bound could be as large as 55 points, but the above is the range of my sample) and mean is around 15 and the data looks pretty normally distributed. Tx is treatment assignment (A or B), and week ranges from 0 to 10. blY is a baseline measure of the outcome Y. The data is collected over 10 weeks, and it is collected biweekly (ie. week 0, 2, 4, 6, 8, 10). There are 100 subjects, two arms (placebo vs. treated group.) I am interested in whether there is a treatment effect (eg. does treatment improve outcome, i.e. is there a reduction in Y over time). Since the data are repeated measurements from the same subjects, I selected AR(1) as the covariance structure since observations farther apart are likely to be less correlated (However, I also specified other correlation structures such as exchangeable and independent).

The issue I have is this: When I fit the above model, I get a huge Deviance and Value/DF, see below:

Criterion DF Value Value/DF

Deviance 705 29253.6097 41.4945 Scaled Deviance 705 709.0000 1.0057 Pearson Chi-Square 705 29253.6097 41.4945 Scaled Pearson X2 705 709.0000 1.0057 Log Likelihood -2324.7329

Why is that, and what can I do about it? Did I misspecify the model? IS this a problem? Or can I ignore it? I also plotted the residuals (histogram and normal probability plot) and those graphs look beautiful (ie, residuals are normally distributed, and range between -17 and +17).

When I look at the parameter estimates, the interaction was not significant (p = .75), but all three main effects were significant(p <.03). If there is a significant main effect but not an interaction effect, how is that interpreted?

Any insight is as always immensely appreciated. Anne K.

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