[R] R: net reclassification index after Cox survival analysis
Frank Harrell
f.harrell at vanderbilt.edu
Thu Nov 8 15:59:49 CET 2012
And just to add a thought: Any method that requires binning of continuous
variables is suspect unless you get unanimous agreement on the category
boundaries across all subjects. And even then continuous measures have many
advantages.
Frank
petretta wrote
> Many thanks for Your time.
>
> M.P.
>
>>
>> On Nov 7, 2012, at 9:59 AM,
> petretta@
> wrote:
>>
>>> Thanks to David Winsemius for the replay. i use the latest update of
>>> Hmisc package and I try as reported in the example:
>>>
>>> set.seed(1)
>>> library(survival)
>>> x1 <- rnorm(400)
>>> x2 <- x1 + rnorm(400)
>>> d.time <- rexp(400) + (x1 - min(x1))
>>> cens <- runif(400,.5,2)
>>> death <- d.time <= cens
>>> d.time <- pmin(d.time, cens)
>>> rcorrp.cens(x1, x2, Surv(d.time, death))
>>>
>>> but to me it appears that NRI and IDi are not reported in the results:
>>>
>>> Dxy S.D. x1 more concordant x2 more concordant
>>> -8.212107e-02 1.370738e-01 4.589395e-01
>>> 5.410605e-01
>>> n missing uncensored Relevant
>>> Pairs
>>> 4.000000e+02 0.000000e+00 1.100000e+01
>>> 4.262000e+03
>>> Uncertain C X1 C X2 Dxy
>>> X1
>>> 1.553380e+05 9.920225e-01 9.258564e-01
>>> 9.840450e-01
>>> Dxy X2
>>> 8.517128e-01
>>
>> The NRI is not reported but an equivalent measure is. As far as getting
>> output that is labeled the way you expect it, I also looked at the
>> PredictABEL::reclassification function help page:
>>
>> "The function also computes continuous NRI, which does not require any
>> discrete risk categories and relies on the proportions of individuals
>> with
>> outcome correctly assigned a higher probability and individuals without
>> outcome correctly assigned a lower probability by an updated model
>> compared with the initial model."
>>
>> I think that is essentiality what rcorrp.cens is providing, just not with
>> the labels you expected.
>>
>> " The function requires predicted risks estimated by using two separate
>> risk models. Predicted risks can be obtained using the
>> functionsfitLogRegModel and predRisk or be imported from other methods or
>> packages."
>>
>> So it would seem that you could use results from any censored survival
>> models that had a predict method.
>>
>> --
>> David.
>>
>>
>>>
>>> but only after:
>>>
>>> #rcorrp.cens(x1, x2, y) ## no censoring
>>> set.seed(1)
>>> x1 <- runif(1000)
>>> x2 <- runif(1000)
>>> y <- sample(0:1, 1000, TRUE)
>>> rcorrp.cens(x1, x2, y)
>>> improveProb(x1, x2, y)
>>>
>>> thus censoring not allowed. Or I'm in error?
>>>
>>> Many thanks
>>>
>>> David Winsemius <
> dwinsemius@
> > ha scritto:
>>>
>>>>
>>>> On Nov 7, 2012, at 6:54 AM,
> petretta@
> wrote:
>>>>
>>>>> Dear all,
>>>>>
>>>>> I am interested to evaluate reclassification using net
>>>>> reclassification improvement and Integrated Discrimination Index IDI
>>>>> after
>>>>> survival analysis (Cox proportional hazards using stcox). I search a R
>>>>> package or a R code that specifically addresses the categorical NRI
>>>>> for
>>>>> time-to-event data in the presence of censored observation and, if
>>>>> possible, at different follow-up time points.
>>>>> I know that the ‘PredictABEL’ Package contains functions for NRI
>>>>> and IDI
>>>>> calculation but it is unclear for me if it allows censored
>>>>> observation.
>>>>> Package ‘survIDINRI’ calculates only continuous NRI and the
>>>>> function of
>>>>> Package ‘Hmisc’[#rcorrp.cens(x1, x2, y) ##] is only for no
>>>>> censored
>>>>> observations.
>>>>
>>>> ???. Doesn't its name , 'rcorrp.cens' suggest otherwise? Not to mention
>>>> its description int the Hmisc Index: "Rank Correlation for Paired
>>>> Predictors with a Possibly Censored Response, and Integrated
>>>> Discrimination Index". rcoop.cens is a fairly recent addition to Hmisc
>>>> and I am looking at Hmisc version 3.10-1. If you are looking at a
>>>> version that is a couple of years old, you may be seeing something
>>>> different. The argument list you list looks like the one for
>>>> improveProb(), which does not appear to handle censoring. The
>>>> rcorrp.cens argument list is:
>>>>
>>>> rcorrp.cens(x1, x2, S, outx=FALSE, method=1)
>>>>
>>>> And the "S" object is a Surv-object.
>>>>
>>>>
>>>>> Many thanks.
>>>>>
>>>>> Sincerely,
>>>>>
>>>>> Mario Petretta
>>>>> Dpt. Internal Medicine, Cardiology and Heart Surgery
>>>>> Naples University Federico II - Italy
>>>>>
>>>>> ______________________________________________
>>>>>
> R-help@
> mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>
>>>> David Winsemius, MD
>>>> Alameda, CA, USA
>>>>
>>>>
>>>>
>>>
>>>
>>>
>>> Mario Petretta
>>> Dipartimento di Medicina Clinica Scienze Cardiovascolari e Immunologiche
>>> Facolt� di Medicina e Chirurgia
>>> Universit� di Napoli Federico II
>>> 081 - 7462233
>>>
>>> ______________________________________________
>>>
> R-help@
> mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>
>> David Winsemius, MD
>> Alameda, CA, USA
>>
>>
>>
>
> ______________________________________________
> R-help@
> mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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