Cody Hamilton, Ph.D, wrote:
I have a dataset at a hospital level (as opposed to the patient
level) that contains number of patients experiencing events (call
this number y), and the number of patients eligible for such events
(call this number n). I am trying to model logit(y/n) = XBeta. In
SAS this can be done in PRC LGISTIC or GENMD with a model
statement such as: model y/n = <predictors>;. Can this be done using
lrm from the Hmisc library without restructuring the dataset so that
for each hospital there is one row with y = 1 and one row with y = 0
and then using the weight option in lrm to weight these two responses
by the number of 'successes' and 'failures' for that hospital,
respectively? I would like to avoid the restructuring, and I
understand that the use of the weight function is not compatible with
a lot of the validation functions available in Hmisc (validate,
bootcov, etc.).
Why do you need lrm()? Is there something I'm missing?
As far as I can tell you can simply do
glm(cbind(y,n-y) ~ <predictors>,family=binomial,data=<data>)
where ``<data>'' has columns named ``y'' ``n'' and whatever
the predictors are called.
cheers,
Rolf Turner
rolf (AT) math (DOT) unb.ca
R-help (AT) stat (DOT) math.ethz.ch mailing list
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