On Mar 30, 2010, at 3:45 PM, Michal Figurski wrote:
Dear R-Helpers,
I am a novice in survival analysis. I have the following code:
for (i in 3:12) print(coxph(Surv(time, status)~a[,i], data=a))
I used it to fit the Cox Proportional Hazard models separately for
every available parameter (columns 3:12) in my data set - with
intention to compare the Hazard Ratios.
Of dubious statistical validity at least for modest sample sizes. You
should try that method with randomly generated data and see what you
get.
However, some of my variables are in range 0.1 to 1.6, others in
range 5000 to 9000. How do I compare HRs between such variables?
I have rescaled all the variables to be in 0 to 1 range - is this
the proper way to go?
Seems doubtful. Scaling by the range will let the outliers dominate
the scaling.
Is there a way to somehow calculate the same HRs (as for rescaled
parameters) from the HRs for original parameters?
You could do a lot better by following Frank Harrell's example and use
the difference between the 25th and 75th percentiles as a common
scaling strategy. His anova function provides this as the default.
You are then comparing cases at the boundaries of the upper end of the
lowest quartile with those at the lower end of the upper quartile. No
assumptions of normality need be made and you are much less subject to
the erratic sampling properties of the zeroth and 100th percentiles.
--
David Winsemius, MD
Many thanks in advance.
--
Michal J. Figurski, PhD
HUP, Pathology & Laboratory Medicine
Biomarker Research Laboratory
3400 Spruce St. 7 Maloney
Philadelphia, PA 19104
tel. (215) 662-3413
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David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
