Hi,
I'm using geeglm function to account for the repeated measure.
fit1<- geeglm( binary.outcome ~ age + race + gender + fever.yes.no,
data=mydata, id=ID, family=binomial, corstr="exchangeable")
summary(fit1)$coef gives too large estimates and standard deviation:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 3.07e+16 7.20e+14 1821.29 0.00000
age 6.38e+13 2.22e+13 8.24 0.00409
RACEBlack 1.48e+16 6.28e+14 555.35 0.00000
RACEOther -1.84e+16 6.17e+14 887.78 0.00000
SEXFemale 1.84e+16 5.23e+14 1235.19 0.00000
FEVERYes -4.41e+15 4.74e+14 86.73 0.00000
FEVERUnknown 1.76e+16 1.60e+15 120.55 0.00000
compared to the estimates from the glm model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.65924 6.41e-01 -1.02875 0.3036
age 0.00686 1.16e-02 0.59304 0.5532
RACEBlack 0.60687 4.13e-01 1.46900 0.1418
RACEOther -1.18660 1.24e+00 -0.96054 0.3368
SEXFemale 0.61805 3.57e-01 1.73021 0.0836
FEVERYes -0.96825 3.77e-01 -2.56554 0.0103
FEVERUnknown 0.39761 9.68e-01 0.41087 0.6812
I have 160 observations in my data, and 146 unique ID. Is that the problem?
Because I don't have "enough" repeated measures for each ID?
Thank you very much
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