[R] relation between tdrocc AUC and c-statistic from rcorr.cens
I am using the rcorr.cens function from the Hmisc package and the time-dependent ROC curve obtained using tdrocc in the survcomp package. I understand that the C statistic from rcorr.cens has to be subtracted from 1 if high values of the risk variable lower survival. Given that I wonder what the connection is between that C statistic and the AUC from the tdrocc object. If they are substantially different are there any views on which one is to be preferred ? Many thanks in advance [[alternative HTML version deleted]] __ 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] C-statistics (AUCs) from rcorr.cens or survcomp time-dependent ROC curves
I am using the rcorr.cens function from the Hmisc package and the time-dependent ROC curve obtained using tdrocc in the survcomp package. I understand that the C statistic from rcorr.cens has to be subtracted from 1 if high values of the risk variable lower survival. Given that I wonder what the connection is between that C statistic and the AUC from the tdrocc object. Are they expected to be the same ? Many thanks in advance [[alternative HTML version deleted]] __ 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] confidence intervals for Harrell's c-index in survival setting
Dear All, Is it possible to get confidence intervals for Harrell's concordance index or, equivalently, Somer's D using the rms package or in some other way ? I have survival data it would be the c-index in the Cox model setting Many thanks Dr Bernard North Statistical Consultant Statistical Advisory Service Advice and Courses on Research Design and Methodology Imperial College South Kensington Campus 8 Princes Gardens Room 845, 4th Floor London SW7 1NA. Tel: 020 7594 2034 Fax: 020 7594 1489 Email: bno...@imperial.ac.ukmailto:bno...@imperial.ac.uk Web: www.ic.ac.uk/stathelphttp://www.ic.ac.uk/stathelp [[alternative HTML version deleted]] __ 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] output (p-values) of fastbw in Design package
I am using the validate option in the Design package with the Cox survival model. I am using the bw=T option which, like the fastbw function, performs a backward elimination variable selection The output includes a series of columns (below) giving information on eliminated variables. My question is that I am unsure of the difference between the 2 p-values given (the one after Chi-Sq and the one after df) This may be a gap in my statistics knowledge, hopefully I've not asked too silly a question, many thanks in advance Backwards Step-down - Original Model DeletedChi-Sq d.f. P Residual d.f. P AIC [[alternative HTML version deleted]] __ 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] using axis.Date with interaction.plot
Dear List I want to plot multiple time series (several outcomes) with dates at intervals on the x axis and a legend. Is it possible to use axis.Date to get dates, at intervals in my case, on the x axis of a (multiple) time series plot. The axis.Date in the code below doesn't produce any dates on the x axis. (The difference outcome measures are all variable outcome in dataframe speclong and the different groups correspond to different values of the variable time with values 1 to 8) But the same axis.Date command gives x axis Dates perfectly if following an ordinary plot command. So I suppose I could do that and add time series for successive time series using lines( but then I wouldn't have a legend or I'd have to create one manually Many thanks for any advice Bernard speclong Date time outcome id 1.1 1998-01-291 8.11879e-13 1 2.1 1998-02-171 9.11297e-13 2 3.1 1998-03-201 4.95558e-13 3 4.1 1998-05-071 4.34171e-13 4 5.1 1998-05-261 2.41658e-13 5 6.1 1998-06-051 4.89529e-13 6 82.8 2005-07-158 2.37023e-11 82 83.8 2005-08-238 3.52766e-11 83 84.8 2005-09-088 1.98099e-11 84 85.8 2005-10-188 2.93576e-11 85 86.8 2005-11-188 6.30531e-11 86 87.8 2005-12-018 5.71245e-11 87 interaction.plot(speclong$Date,speclong$time,speclong$outcome,log=y,xaxt=n,col=rainbow(8) ) axis.Date(1,at=(as.Date(1998-01-01) +c(0:7)*365.25),format=%d/%m/%y, las=2) sessionInfo() R version 2.9.0 (2009-04-17) i386-pc-mingw32 locale: LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United Kingdom.1252;LC_MONETARY=English_United Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base [[alternative HTML version deleted]] __ 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.
Re: [R] Nagelkerkes R2N
I am interested Andrea is whether you ever established why your R2 was 1. I have had a similar situation previously. My main issue though, which I'd be v grateful for advice on, is why I am obtaining such negative values -0.3 for Somers Dxy using validate.cph from the Design package given my value of Nagelkerke R2 is not so low 13.2%. I have this output when fitting 6 variables all with p-values0.01 I am wondering what the interpretation should be. I know my Nagelkerke R2 isn't very good but I compare my results with the example from ?validate.cph and although I have a better R2 (13% v 9%) the Somers dxy from the example data set is much better, 38%, so certainly not negative ! So my main question is : Why such a difference between explained variation, R2, and predictive ability: somers dxy ?? Obs Events Model L.R. d.f. P ScoreScore P R2 471228 66.36 6 0 73.41 0 0.132 validate(f, B=150,dxy=T) # normally B=150 index.orig training test optimism index.corrected n Dxy -0.3022537331 -0.3135032097 -0.292492573 -0.021010636 -0.2812430968 150 R2 0.1319445685 0.1431179294 0.122599605 0.0205183240.1114262446 150 Slope 1.00 1.00 0.923340558 0.0766594420.9233405576 150 D 0.0250864459 0.0276820092 0.023163167 0.0045188420.0205676038 150 U -0.0007676033 -0.0007725071 0.000610456 -0.0013829630.0006153598 150 Q 0.0258540493 0.0284545164 0.022552711 0.0059018050.0199522440 150 I also calculated the Schemper and Henderson V measure and obtained v=10.5% I was using the surev package of Lusa Lara; Miceli Rosalba; Mariani LuigiEstimation of predictive accuracy in survival analysis using R and S-PLUS.http://www.biomedexperts.com/Abstract.bme/17601627/Estimation_of_predictive_accuracy_in_survival_analysis_using_R_and_S-PLUS Computer methods and programs in biomedicine 2007;87(2):132-7. And my code was library(surev) pred.accuracy-f.surev(f) pred.accuracy sorry if my question isn't clear - should I have included my sessionInfo for a methodological question ? (I'm a newbie) many thanks for any advice [[alternative HTML version deleted]] __ 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] negative Somers D from Design package
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 -0.55407 -0.566027923 -0.0006374833-0.565390440 150 R2 0.325860603 0.327350885 0.325860603 0.0014902826 0.324370320 150 Slope 1.0 1.0 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 8 Princes Gardens London SW7 1NA. Tel: 020 7594 2034 Fax: 020 7594 1489 Email: bno...@imperial.ac.uk Web: www.ic.ac.uk/stathelp [[alternative HTML version deleted]] __ 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.
