Dear all,
I want to fit a random-coefficient/slope model with a binary outcome
(logit) for a meta-analysis. We tried to use xtmelogit. However,
xtmelogit seems to automatically assume a random intercept whereas we
want the intercept to be a fixed effect. Does anybody know whether it is
possible to use xtmelogit/poisson with a random-coefficient only and if
yes, how this is specified (we were not able to find anything on the
web/statalist archive)?
We also tried two different approaches in GLLAMM which gave quite
different results. The first approach is shown below:
Approach based on gllamm manual (example 9.5):
gen cons=1
eq int: cons
eq slope: treatment
gllamm success treatment, i(id) nrf(2) eqs(int slope) l(logit)
(binom) weight(wt) adapt nocor trace allc
However, this approach also includes a random intercept. Does anybody
know whether it is possible to specify a fixed-intercept and
random-coefficient model in gllamm (the noconstant option doesn't seem
right because it gave implausible results)?
Any help is appreciated
Thanks in advance
Sven
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