I am doing a logistic regression: Y = Treatment group + Covariate1 + Covariate2 ..+Covariate4. Y is a binomial outcome variable: patient response, patient non-response. Treatment group is the factor I am interested in, which has Group A and Group control. The other 4 covariates are all categorical, some has two levels, some has more. For those covariates with more than 2 levels, dummy covariates are used. Each patient has only one observation, that is, no repeated measures. I want to get such results from the model: Odds ratio of treatment group, and its 95% CI, p-value, and the response rate in each treatment group adjusted for the covariates. Regarding OR, CI and p-value, I get the same results from PROC LOGISTIC and PROC GENMOD. The problem is the response rate in each treatment group adjusted for the covariates. My questions are: 1. I don't find any option in both PROCs to provide such an adjusted response rate for both treatment groups. Is anybody know such options? 2. I calculated the adjusted response rate in this way: firstly, I get predicted probability(p) from the model for each patient; then, I calculate the logit for each patient, which is log(p/(1-p)) ; thirdly, I calculate the mean logit in both treatment groups; fourthly, derive the adjusted response rate of each treatment group = exp(MeanLogit) / (1+exp(MeanLogit) ) I am not sure if my calculation steps are correct. Can anyone give me comments? 3. The predicted probability from PROC LOGISTIC and PROC GENMOD are totally different, even not on the same magnitude. I don't understand. Can anyone explain? Therefore, my above calculated adjusted response rates from both models are very different. I don't know which one to trust. Many thanks in advance for any help!!!
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