On 8 Apr 2008, at 03:11, Ashwin Ananthakrishnan wrote:
I am currently using Stata 9.2. My analysis involves
logistic regression of a dichotomous outcome (dead /
alive) across various predictors. My main predictor of
interest is hospital type (Small, medium, big). How do
I adjust for clustering within each type of hospital?
There are two things to consider. The first is that hospital type may
influence prognosis, so that the death rate is different in each
type. You can stratify your analysis by hospital, or you can include
hospital size as a predictor variable.
If you have a variable coded 1 to 3 for small, medium and large
hosptals, and a variable that identifies each hospital
xi: logistic dead i.hospital_size p1 p2 p3, cluster(hospital_id)
looks at the effects of three predictors (p1 to p3) and uses -xi- to
create dummy variables to represent hospital size. The cluster option
allows for clustering within hospitals.
The largest of the 'big' hospitals may contribute more
patients to the analysis than the smaller of the 'big'
hospitals. Do I need to adjust for this phenomenon?
i.e. if we hypothesize that larger hospitals have
better outcomes, isn't it appropriate that the larger
of the big hospitals contribute more patients and
hence have a larger weight?
Larger hospitals contribute more patients and so their death rate can
be estimated with greater precision. But downweighting them for this
reason makes no sense.
Or do I need to adjust for this? and how do I do it?
P Before printing, think about the environment
=================================
Ronan Conroy
[email protected]
Royal College of Surgeons in Ireland
Epidemiology Department,
120 St Stephen's Green, Dublin 2, Ireland
+353 (0)1 402 2431
+353 (0)87 799 97 95
http://www.flickr.com/photos/ronanconroy/sets/72157601895416740/
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