similar to: Concordance Index - interpretation

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

Dear all, As I said in my previous email I have just upgraded to R 2.14.0 on Windows 7. I have just run the 'coxph' function and notice that a Concordance statistic is produced. Is there any way to extract this information from the output? E.g. can I call the concordance value independently of calling the output? Many thanks, Laura [[alternative HTML version deleted]]

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducible the assay

Hello group, my question is deciding what test would be appropriate for following question. An experiment 'A' yielded 3200 observations of which 431 are significant. Similarly, using same method, another experiment 'B' on a different population yielded 2541 observations of which 260 are significant. There are 180 observations that are common between significant observations of A

Dear I estimate a sample selection model using the Clayton copula and Burr and Gaussian marginal. I need to derive ther Kendall'sw tau from the concordance coefficient by integration. I came across a way to do that in R long time ago but cannot find it again. Can somewone tell me what to read and what to use? Thank you. Steven Yen

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

Hi, can anybody tell me how to contact the maintainer of the "concord" package? The address given in the help file is not valid anymore. ,---- | > help(package=concord) | | Information f?r Paket 'concord' | | Description: | | Package: concord | Version: 1.4-2 | Date: 2005-05-10 | Title: Concordance and reliability | Author: Jim Lemon

I have two alternative Cox models with C-statistics 0.72 and 0.78. My question is if 0.78 is significantly greater than 0.72. I'm using rcorrp.cens. I cannot find the U statistics in the output of the function. This is the output of the help example: > x1 <- rnorm(400) > x2 <- x1 + rnorm(400) > d.time <- rexp(400) + (x1 - min(x1)) > cens <- runif(400,.5,2) > death

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

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 All, I am new to R and am trying to learn how to create functions using R. Below is code which calculates Lin's Concordance Coefficient. After I calculate the coefficient I want to create a scatter plot which annotates the coefficient along with preceding text onto the plot. The below code doesn't seem to work. If I use only the object 'lincc' on the text command it works

The survival package, like many others, has several helper functions that are not declared in the namespace, since their only use is to be called by other "main" functions of the package.? This works well since the functions in the survival namespace can see them --- without ::: arguments --- and others don't. Until a situation I ran into this week, for which I solicit comments

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

Dear Prof. Therneau I was impressed to discover that the 'survConcordance' now handles Surv() objects in counting format (example below to clarify what I mean). This is not documented in the help page for the function. I am very curious to see how a c-index is estimated in this case, using just the linear predictors. It was my impression that with left truncation the ordering of

I have a data with binary response variable, repcnd (pregnant or not) and one predictor continuous variable, svl (body size) as shown below. I did Hosmer-Lemeshow test as a goodness of fit (as suggested by a kind “R-helper” previously). To test whether the predictor (svl, or body size) has significant effect on predicting whether or not a female snake is pregnant, I used the differences between

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

The xdvi and yap DVI viewers allow inverse (aka reverse) search: you click on a location in the output display, and your editor moves to the corresponding location in the input file. yap at least also allows forward search, where you can have your editor tell it to location where a particular input line is displayed. I would like something like this to work in Sweave files. That is, I

I am trying to fit a nonlinear regression model to data. There are several predictor variables and 8 parameters. I will write the model as Y ~ Yhat(theta1,...,theta8) OK, I can do this using nls() - but "only just" as there are not as many observations as might be desired. Now the problem is that we have a factor "Site" and I want to include a corresponding error

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,