Frank,
Thank you for your quick response!
I want to compare the discriminative capacity of different
anthropometric measures in predicting mortality, focussing on the "thin"
site of these measures.
Since these associations are not linear (U shaped for BMI and inversily
J-shaped for mid-upper arm circumference) and I do not want to include
the prediction by "obesity", I am using all values below the median of
each separate measure to calculate a C-statistic (below the median, the
association is approximately linear).
As a result, some different and some overlapping cases are included.
I understand your point though.
Any suggestion is welcome.
Hanneke
Frank E Harrell Jr schreef:
Hanneke Wijnhoven wrote:
Does anyone know of an R-function or method to compare two
C-statistics (Harrells's C - rcorr.cens) obtained from 2 different
models in partially paired datasets (i.e. some similar and some
different cases), with one continuous independent variable in each
separate model? (in a survival analysis context)?
I have noticed that the rcorrp.cens function can be used for paired
data.
Thanks for any help,
Hanneke Wijnhoven
Hanneke,
I'm having trouble seeing how the unpaired observations can contribute
information in general. If for example all of the observations were
unpaired, one C-statistic might be larger because it came from a
dataset with more extreme observations that were easier to discriminate.
Frank
--
Hanneke A.H. Wijnhoven (PhD)
Institute of Health Sciences
Vrije Universiteit Amsterdam
De Boelelaan 1085
1081 HV Amsterdam
The Netherlands
Tel. +31 (0) 20 5989951
Fax. +31 (0) 20 5986940
hanneke.wijnho...@falw.vu.nl
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