Not sure about your data set, but if you have some kind of
(weighted/stratified) sample of hospitals you need to pay special
attention. Survey data violates the assumptions of the classical
linear models (infinite population, identically distributed errors
etc) and needs to be analyzed differently. In SAS, it's wrong to throw
such data into a PRC LGISTIC / REG; PRC SURVEYLGISTIC / SURVEYREG
should be used instead. In R, take a look at the survey package. For
details check
Message
From: r-help-bounces (AT) stat (DOT) math.ethz.ch
[mailto:r-help-bounces (AT) stat (DOT) math.ethz.ch] Behalf Hamilton, Cody
Sent: Friday, June 16, 2006 1:32 PM
To: r-help (AT) stat (DOT) math.ethz.ch
Subject: [R] modeling logit(y/n) using lrm
--
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.).
>
>
>
Cody Hamilton, Ph.D
Institute for Health Care Research and Improvement
Baylor Health Care System
(214) 265-3618
>
>
>
>
>
This e-mail, facsimile, or letter and any files or
attachments transmitted with it contains information that is
confidential and privileged. This information is intended
only for the use of the individual(s) and entity(ies) to whom
it is addressed. If you are the intended recipient, further
disclosures are prohibited without proper authorization. If
you are not the intended recipient, any disclosure, copying,
printing, or use of this information is strictly prohibited
and possibly a violation of federal or state law and
regulations. If you have received this information in error,
please notify Baylor Health Care System immediately at
1-866-402-1661 or via e-mail at privacy (AT) baylorhealth (DOT) edu.
Baylor Health Care System, its subsidiaries, and affiliates
hereby claim all applicable privileges related to this information.
[[alternative HTML version deleted]]
R-help (AT) stat (DOT) math.ethz.ch mailing list
PLEASE do read the posting guide!
R-help (AT) stat (DOT) math.ethz.ch mailing list
PLEASE do read the posting guide!