On 3 Dec 2008, at 16:32, Scott Gilmore wrote:
I have a dataset of N=50 patients and 20 variables. 13 are
categorical and 7 are continuous variables. I have no missing
values. My outcome is one of the variables and it is a binary
outcome, 1= yes disease, 0= no disease.
Looks like a nightmare to me - I have had someone in my office with
almost exactly the same ratio of patients to variables. The trouble is
that you don't have enough data. And a stepwise model will shrink
badly when applied to new data, so the clinical validity of the
exercise is very doubtful.
I might recommend -mrgraph- for inspecting the binary variables - with
the -tab- option you get a nice 'northen blot'
Try also clustering routines to see if you can make any sense of the
predictors.
But avoid statistical significance tests for the moment. Your chances
of false negative results are very high given the sample size, and
stepwise methods will only confuse the issue by capitalising on
unreproducible features of your data.
Ronan Conroy
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Royal College of Surgeons in Ireland
Epidemiology Department,
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+353 (0)1 402 2431
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http://rcsi.academia.edu/RonanConroy
P Before printing, think about the environment
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