For the Cox model Dxy is the rank correlation between predicted log
hazard and time to event. As a high hazard means that the time to event
is short, you need to negate Dxy for your purpose.
Frank
North, Bernard V wrote:
Dear R help
My problem is very similar to the analysis detailed here.
If we use the mayo dataset provided with the survivalROC package the estimate
for Somer's Dxy is very negative -0.56.
The Nagelkerke R2 is positive though 0.32.
I know there is a difference between explained variation and predictive ability
but I am surprised there is usch a difference given that even a non predictive
model should have Dxy around 0.
Am I doing something wrong or is there an interpretation that makes sense ?
This is with the mayo data so its reproducible but the result with my data is
very similar.
Many thanks in advance
library(survivalROC)
library(Design)
library(survival)
data(mayo)
Sm <- Surv(mayo$time,mayo$censor)
fm <- cph( Sm ~ mayoscore4,mayo,x=T,y=T,surv=T )
validate(fm, B=150,dxy=T)
Iteration 1
index.orig training test optimism index.corrected n
Dxy -0.566027923 -0.55407 -0.566027923 -0.0006374833-0.565390440 150
R2 0.325860603 0.327350885 0.325860603 0.0014902826 0.324370320 150
Slope 1.0 1.0 0.987854765 0.0121452354 0.987854765 150
D 0.093398440 0.095166239 0.093398440 0.0017677983 0.091630642 150
U -0.001562582 -0.001579618 0.001150175 -0.0027297932 0.001167211 150
Q 0.094961022 0.096745857 0.092248266 0.0044975915 0.090463431 150
Dr Bernard North
Statistical Consultant
Statistical Advisory Service
Advice and Courses on Research Design and Methodology
Imperial College
South Kensington Campus
Room 845, 4th Floor
8 Princes Gardens
London SW7 1NA.
Tel: 020 7594 2034
Fax: 020 7594 1489
Email: bno...@imperial.ac.uk
Web: www.ic.ac.uk/stathelp
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