similar to: rcorrp.cens and U statistics

Dear R-listers, I am an MD and clinical epidemiologist developing a measure of comorbidity severity for patients with liver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with the "old" comorbidity measure, Charlson's Comorbidity Index. I have nearly 10,000

I am reposting my message from April 8th because I never received a response to the original post: Dear R-listers, I am an MD and clinical epidemiologist developing a measure of comorbidity severity for patients with liver disease. Having developed my comorbidity score as the linear predictor from a Cox regression model I want to compare the discriminative ability of my comorbidity measure with

Hi R-users, I'm having some problems in using the Hmisc package. I'm estimating a cox ph model and want to test whether the drop in concordance index due to omitting one covariate is significant. I think (but I'm not sure) here are two ways to do that: 1) predict two cox model (the full model and model without the covariate of interest) and estimate the concordance index (i.e. area

Dear, I wanna to compare AUC generated by two distribution models using the same sample. I tried improveProb function's example code below. 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))

Dear R-help, Would it be appropriate to do the following to calculate a p-value for the difference between c-ind of x1 and c-inx of x2 using the output from rcorrp.senc() > r<-rcorrp.senc(x1,x1,y) > pValue<-1-pnorm((r[11]-r[12])/(r[2]/r[5])*1.96) Osman O. Al-Radi, MD, MSc, FRCSC Chief Resident, Cardiac Surgery University of Toronto, Canada

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

Dear R-help, This is an example in the {Hmisc} manual under rcorr.cens function: > set.seed(1) > x <- round(rnorm(200)) > y <- rnorm(200) > round(rcorr.cens(x, y, outx=F),4) C Index Dxy S.D. n missing uncensored Relevant Pairs Concordant Uncertain 0.4831 -0.0338 0.0462 200.0000

At work after updating to R 2.5 I get an error using epitab from package epitools, when at home (R 2.4) I get no error. Could someone help me? Thanks Pietro Bulian Servizio di Onco-Ematologia Clinico-Sperimentale I.R.C.C.S. Centro di Riferimento Oncologico Via Franco Gallini 2 33081 Aviano (PN) - Italy phone: +39 0434 659 412 fax: +39 0434 659 409 e-mail: pbulian at cro.it (at work)

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

Dear experts, when I call the step function for a coxph model with n covariates and a dicotomous variable included as strata, the first term removed by step is always the strata variable. This is not what I want and then I do a manual step updating the model minus the least significant covariate and testing with anova, until I have minimized the model. Is there a package were this can be done? or

Hi, Is there any package or code to compare and display correlated ROC curves in R? Thanks, Reza [[alternative HTML version deleted]]

Hello, Is there is any R function computes the AUC for paired data? Many thanks, Samuel [[alternative HTML version deleted]]

Hello, I'm a biostatistician in Toronto. I would like to know if there is anything in survival analysis developed in R for the method "Evaluating the Yield of Medical Test" (JAMA. May 14,1982--Vol 247, No.18 Frank E. Harrell, Jr,PhD; Robert M. Califf, MD; David B. Pryor, MD;Kerry L.Lee, PhD; Robert A. Rosait,MD.) Hope to hear from you and thanks Lisa Wang, MSc Project Organiser

Hello everyone. This is a question regarding generation of the concordance index (c index) in R using the function rcorr.cens. In particular about interpretation of its direction and form of the 'predictor'. One of the arguments is a "numeric predictor variable" ( presumably this is just a *single* predictor variable). Say this variable takes numeric values.... Am I

Hello useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports.

Dear all, I have a question on Cohen's kappa: Assume I have two datasets, one has 500 objects, 10 methods and the other, 1000 different objects, 20 different methods. Could I compare between the two datasets to conclude the 10 methods are more "concordant" than the 20 ones by looking at some output, for example, cohen.kappa{concord} ? One more, could anyone explain in brief,

Dear R-helpers, I recently switched from SAS to R, in order to model the occurrence of rare events through logistic regression. Is there a package available in R to calculate the Goodman-Kruskal Gamma? After searching a bit I found a function "rcorr.cens" which should do the job, but it is not clear to me how to define the input vectors? Is "x" a vector with the fitted

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

Dear List, I would like to calculate the Goodman-Kruskal gamma for the predicted classes obtained from an ordinal regression model using lrm in the Design package. I couldn't find a way to get gamma for predicted values in Design so have found previous positings suggesting to use : Rcorr.cens(x, S outx = TRUE) in the Hmisc package My question is, will this work for predicted vs

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for