[R] How to test if two C statistics are significantly different?

[Frank Harrell]

Thanks for mentioning rcorrp.cens which is much more powerful than testing
for differences in C.  Likelihood ratio tests would be even more powerful. 
Ordinary differences in C index yields a test with power that is too low.
Frank

alanm (Alan Mitchell) wrote:
> 
> ?Hmisc::rcorrp.cens
> 
> -Alan
> 
> 
> 
> -----Original Message-----
> From: Eik Vettorazzi [mailto:E.Vettorazzi@]
> Sent: Tue 10/11/2011 2:25 AM
> To: Yujie Wang
> Cc: r-help@
> Subject: Re: [R] How to test if two C statistics are significantly
> different?
>  
> Hi Yujie,
> there is still a lot of work in progress, I think. As
> http://faculty.washington.edu/heagerty/Software/SurvROC/RisksetROC/risksetROCdiscuss.pdf
> 
> states: "[...] for inference and variance estimation, we now suggest
> bootstrapping [...]".
> Recently I catched a glimpse on roc.test from the pROC package, they
> implemented, amongst others, a bootstrap algorithm - maybe this is a
> start for your own work?
> 
> Hth.
> 
> Am 10.10.2011 21:35, schrieb Yujie Wang:
>> Hey all,
>> 
>> In order to test if a marker is a risk factor, I built two models (using
>> cox
>> proportional hazard model). One model included this marker, and the other
>> is
>> not.
>> 
>> Then, I use R package risksetROC to test how much predictive value did
>> the
>> marker add to this model. I get two C statistics by analyzing the linear
>> predictors of the two models into this package.
>> 
>> The qustion is How to test if two C statistics are significantly
>> different?
>> 
>> Your help will be greatly appreciated!
>> 
>> Yujie
>> 
>> 	[[alternative HTML version deleted]]
>> 
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> 
> 
> -- 
> Eik Vettorazzi
> Institut für Medizinische Biometrie und Epidemiologie
> Universitätsklinikum Hamburg-Eppendorf
> 
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> 
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> 


-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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