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 -0.566665407 -0.566027923 -0.0006374833 -0.565390440
150
> R2 0.325860603 0.327350885 0.325860603 0.0014902826 0.324370320
150
> Slope 1.000000000 1.000000000 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
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> London SW7 1NA.
>
> Tel: 020 7594 2034
> Fax: 020 7594 1489
> Email: bnorth at imperial.ac.uk
> Web: www.ic.ac.uk/stathelp
>
>
>
>
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>
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University