Hi Kohkichi,
What we really need to figure out is how to make validate give you
confidence intervals for Dxy or C while it is penalizing for overfitting.
Some people have ad hoc solutions for that but nothing is nailed down yet.
Frank
khosoda wrote:
>
> Thank you for your comment, Prof Harrell.
>
> I changed the function;
>
> CstatisticCI <- function(x) # x is object of rcorr.cens.
> {
> se <- x["S.D."]/2
> Low95 <- x["C Index"] - 1.96*se
> Upper95 <- x["C Index"] + 1.96*se
>
> cbind(x["C Index"], Low95, Upper95)
> }
>
> > CstatisticCI(MyModel.lrm.penalized.rcorr)
> Low95 Upper95
> C Index 0.8222785 0.7195828 0.9249742
>
> I obtained wider CI than the previous incorrect one.
> Regarding your comments on overfitting, this is a sample used in model
> development. However, I performed penalization by pentrace and lrm in
> rms package. The CI above is CI of penalized model. Results of
> validation of each model are followings;
>
> First model
> > validate(MyModel.lrm, bw=F, B=1000)
> index.orig training test optimism index.corrected n
> Dxy 0.6385 0.6859 0.6198 0.0661 0.5724 1000
> R2 0.3745 0.4222 0.3388 0.0834 0.2912 1000
> Intercept 0.0000 0.0000 -0.1446 0.1446 -0.1446 1000
> Slope 1.0000 1.0000 0.8266 0.1734 0.8266 1000
> Emax 0.0000 0.0000 0.0688 0.0688 0.0688 1000
> D 0.2784 0.3248 0.2474 0.0774 0.2010 1000
> U -0.0192 -0.0192 0.0200 -0.0392 0.0200 1000
> Q 0.2976 0.3440 0.2274 0.1166 0.1810 1000
> B 0.1265 0.1180 0.1346 -0.0167 0.1431 1000
> g 1.7010 2.0247 1.5763 0.4484 1.2526 1000
> gp 0.2414 0.2512 0.2287 0.0225 0.2189 1000
>
> penalized model
> > validate(MyModel.lrm.penalized, bw=F, B=1000)
> index.orig training test optimism index.corrected n
> Dxy 0.6446 0.6898 0.6256 0.0642 0.5804 1000
> R2 0.3335 0.3691 0.3428 0.0264 0.3072 1000
> Intercept 0.0000 0.0000 0.0752 -0.0752 0.0752 1000
> Slope 1.0000 1.0000 1.0547 -0.0547 1.0547 1000
> Emax 0.0000 0.0000 0.0249 0.0249 0.0249 1000
> D 0.2718 0.2744 0.2507 0.0236 0.2481 1000
> U -0.0192 -0.0192 -0.0027 -0.0165 -0.0027 1000
> Q 0.2910 0.2936 0.2534 0.0402 0.2508 1000
> B 0.1279 0.1192 0.1336 -0.0144 0.1423 1000
> g 1.3942 1.5259 1.5799 -0.0540 1.4482 1000
> gp 0.2141 0.2188 0.2298 -0.0110 0.2251 1000
>
> Optimism of slope and intercept were improved from 0.1446 and 0.1734 to
> -0.0752 and -0.0547, respectively. Emax was improved from 0.0688 to
> 0.0249. Therefore, I thought overfitting was improved at least to some
> extent. Well, I'm not sure whether this is enough improvement though.
>
> --
> Kohkichi
>
> (11/05/22 23:27), Frank Harrell wrote:
>> S.D. is the standard deviation (standard error) of Dxy. It already
>> includes
>> the effective sample size in its computation so the sqrt(n) terms is not
>> needed. The help file for rcorr.cens has an example where the confidence
>> interval for C is computed. Note that you are making the strong
>> assumption
>> that there is no overfitting in the model or that you are evaluating C on
>> a
>> sample not used in model development.
>> Frank
>>
>>
>> Kohkichi wrote:
>>>
>>> Hi,
>>>
>>> I'm trying to calculate 95% confidence interval of C statistic of
>>> logistic regression model using rcorr.cens in rms package. I wrote a
>>> brief function for this purpose as the followings;
>>>
>>> CstatisticCI<- function(x) # x is object of rcorr.cens.
>>> {
>>> se<- x["S.D."]/sqrt(x["n"])
>>> Low95<- x["C Index"] - 1.96*se
>>> Upper95<- x["C Index"] + 1.96*se
>>> cbind(x["C Index"], Low95, Upper95)
>>> }
>>>
>>> Then,
>>>
>>>> MyModel.lrm.rcorr<- rcorr.cens(x=predict(MyModel.lrm), S=df$outcome)
>>>> MyModel.lrm.rcorr
>>> C Index Dxy S.D. n
>>> missing uncensored
>>> 0.8222785 0.6445570 0.1047916 104.0000000
>>> 0.0000000 104.0000000
>>> Relevant Pairs Concordant Uncertain
>>> 3950.0000000 3248.0000000 0.0000000
>>>
>>>> CstatisticCI(x5factor_final.lrm.pen.rcorr)
>>> Low95 Upper95
>>> C Index 0.8222785 0.8021382 0.8424188
>>>
>>> I'm not sure what "S.D." in object of rcorr.cens means. Is this standard
>>> deviation of "C Index" or standard deviation of "Dxy"?
>>> I thought it is standard deviation of "C Index". Therefore, I wrote the
>>> code above. Am I right?
>>>
>>> I would appreciate any help in advance.
>>>
>>> --
>>> Kohkichi Hosoda M.D.
>>>
>>> Department of Neurosurgery,
>>> Kobe University Graduate School of Medicine,
>>>
>>> ______________________________________________
>>> R-help@r-project.org 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
>> --
>> View this message in context:
>> http://r.789695.n4.nabble.com/How-to-calculate-confidence-interval-of-C-statistic-by-rcorr-cens-tp3541709p3542163.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
>> ______________________________________________
>> R-help@r-project.org 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.
>
> ______________________________________________
> R-help@r-project.org 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
--
View this message in context:
http://r.789695.n4.nabble.com/How-to-calculate-confidence-interval-of-C-statistic-by-rcorr-cens-tp3541709p3542654.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
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