How to calculate confidence interval of C statistic by rcorr.cens

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How to calculate confidence interval of C statistic by rcorr.cens

 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, ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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Re: How to calculate confidence interval of C statistic by rcorr.cens

 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 細田弘吉 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, ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code. Frank Harrell Department of Biostatistics, Vanderbilt University
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