Hanneke Wijnhoven wrote:
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
Subsetting the data will make the two task difficulties unequal, I fear.
This would make it difficult to compare predictive discrimination indexes.
I think it would be better to fit splines to the continuous predictors,
to allow for a unified analysis over the whole range. Then everything
is paired.
Frank
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
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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