Dear All,
Thank you for your interest in my problems. I really appreciate all your suggestions and comments. I have few comments as well:
- Michael, in one of your posts you mentioned "If the response variable in the GEE is the same binary variable that you are using in the logistic regression model I don't see where it makes any difference"
- The difference is in the interpretation of the parameter estimates. GEE is a mraginal effects model and the parameter estimates are used to draw inferences about population averaged effects, while Mixed effect logistic regression model is a subject specific model and the parameter estimates are used to draw inferences specific to subjects. Depending upon what our study objective is we would fit different models. Does the group agree with me?
- Michael and Robert thank you for the suggestion of GEE model with a structured covariance it works when I replace type from unstructured to compound symmetry. Just one more question though, in the output just below the working correlation matrix it specifies Exchangeable working correlation =-0.01757. Is there a way I can test for it's significance? The group was suspecting some correlation between responses of the patients seeing the same physician. Also, I get the following warning:
- Michael, the code that I sent for PROC NLMIXED had initial parameter estimates commented because I first tried to run the model with initial parameter estimates and when they didn't converge then I tried to see what happens if I use the default starting values and had no luck then too. The initial parameter estimates that I was using were the estimates obtained form logistic regression model.
PS: There are 10 physicians (clusters) who only have one patient. Thus, I have 10 clusters of size one.
- Robert when you ask to constrain my correlation matrix in NLN what do you mean? I am sorry but I don't get it. Following is the code I am using
PROC NLMIXED DATA=abc;
parms b0=0.1922 b1= 0.4 b2= 0.8 b3=0.370 b4=1.3 b5=0.23 b6=11.97 b7=1 b8=1.4 b9=0.5;
xb = b0 +u+ .....;
p = exp(xb)/(1+exp(xb));
model y ~ binary(p);
random u ~ normal(0,s2u) subject=PHYSICIAN;
run;
Once again, thank you for all your suggestions. They are immensely helpful.
I look forward for any further comments and suggestions.
Best Regards,
Tasneem
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[Tasneem] [Zaihra]
[Assistant Professor]
[Concordia University]
[Montreal]
[QC]
[Canada]
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Original Message:
Sent: 03-25-2012 12:15
From: Michael Chernick
Subject: NLMIXED and GEE for binary response not converging
I can't comment on your second question but will comment on the first. If the response variable in the GEE is the same binary variable that you are using in the logistic rgeression model I don't see where it makes any difference. Both are using the covariates to predict the binary outcome. Maybe the logistic regression model is not converging for the same reason as happened with NLMIX and GEE, not enough data to fit so many parameters (n<p). The solution would be either to reduce the number of variables in the logistic regression model or just use the GEE model with a structured covariance. The fixed effects model provides a clue as to which variable to drop, possibly gender, xx1 and x2.
The number of covariates in the fixed effects model is only 7 with 566 observations. So it could only be that in the mixed model you are adding too many correlation parameters.
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Michael Chernick
Director of Biostatistical Services
Lankenau Institute for Medical Research
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