Hello,
I'm trying to model the change of hormones over time on a
group of 80 people. I have measurements at 3 points in time. The
hormones have high variability at all the time points. Quite a few of
them have undetectable values (set to 0) at all the time points. At
time 1, almost 10% are undetectable, and this increases to almost 25%
at the third time point.
I used a GLM with gamma family and log link with a robust calculation
of standard error to model the data. The model is significant for the
time variable. Due to the high variability in the hormones, when I try
to plot the fitted line (exponentiated) on to the scatter plot of the
data, it is hardly visible. Is it correct to plot the
"un-exponentiated" fitted line on a scatter plot with a log
transformed Y-axis? Since I have used the GLM and not explicitly
transformed the dependent variable, I'm not sure whether this is a
correct way to go about it.
Secondly, I tried to use create a random effects model as a better
model for the above data. I used a random intercept and a random slope
for the time variable.
gen cons=1
eq inter: cons
eq slope: time
gllamm H_1 time, i(ID) nrf(2) nip(20) eqs(inter slope) family(gamma)
link(log) adapt
stata gave me an error that the log-likelihood cannot be computed.
For this hormone I have another variable in which the undetectable
values are set to a lower limit of detection. I tried running the
above GLAMM model with that variable and stata was able to calculate
the model. Why would this be the case?
Any help is appreciated.
Thanks,
Leny
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