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 PROC LOGISTIC or GENMOD 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
[EMAIL PROTECTED]
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