similar to: competing risks regression

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

Dear all, I am totally confused by the following R output, but don't have a clue for it. > a <- 1 - 0.2 > a == 0.8 [1] TRUE > a <- 1 - 0.8 > a == 0.2 [1] FALSE > a <- 1 - 0.5 > a == 0.5 [1] TRUE > a <- 1 - 0.6 > a == 0.4 [1] TRUE > a <- 1 - 0.9 > a == 0.1 [1] FALSE My R version is Windows XP R version 2.8.1 (2008-12-22).

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Dear R users I would like to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause

Hi, I posted this question on stats.stackexchange.com 3 days ago but the answer didn't really address my question concerning the speed in competing risk regression. I hope you don't mind me asking it in this forum: I?m doing a registry based study with almost 200 000 observations and I want to perform a competing risk analysis. My problem is that the crr() in the cmprsk package is

Dear R-users Has anybody implemented a function/package that will compute an individual's risk of an event in the presence of competing risks, adjusted for the individual's covariates? The only thing that seems to come close is the cuminc function from cmprsk package, but I would like to adjust for more than one covariate (it allows you to stratify by a single grouping vector). Any

Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script : cov=cbind(x1,x2) z<-crr(ftime,fstatus,cov)) and data file: x1 x2 fstatus ftime 0 .02 1 263 0 .03 1 113 0 .03 1 523

Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks

All, I am working with Emergency Department (ED) Length of Stay Data. The ED visit can end in one of a variety of ways (Admit, discharge, transfer, etc...) Initially, I have modeled the time to event by fitting a survival model to the time the outcome of interest and treat all other outcomes as censoring. However I recently came across the cmprsk package in R which seems to be developed

Hi R users, I'd like to draw a nomogram using a competing-risks regression (crr function in R), rather than a cox regression. However, the nomogram function provided in the Design package is not good for this purpose. Do you have any suggestion. I really appreciate your help Many thanks F.Abdollah, MD San-Raffele hospital Milan, Italy -- View this message in context:

Dear all, I used cph() function from Frank harrell's Design package to create a survival model, then used functions 'Function' and 'sascode' to generate prediction equation based on the saved survival model. But it failed. I included a stratified variable in the model. If I removed the stratification, they were working well. Does that mean that function 'Function'

'make check-all' for current R has been showing this error in the middle for a few months now - any thought on fixing this? I think cmprsk should be either included in the recommended bundle, or the survival vignette to not depend on it. Having 'make check-all' showing glaring ERROR's for a few months seems to defeat the purpose of doing any checking at all via 'make

------------------------------ On Sat, May 16, 2015 8:04 AM BST Uwe Ligges wrote: >Not sure why this goes to R-devel. You just could have asked the >maintainer. Terry Therneau is aware of it and promised he will fix it. > The quickest fix is to add cmprsk to the recommended list , and that's is an R-devel issue. >On 16.05.2015 07:22, Hin-Tak Leung wrote: >> 'make

------------------------------ On Sat, May 16, 2015 2:33 PM BST Marc Schwartz wrote: > >> On May 16, 2015, at 6:11 AM, Hin-Tak Leung <htl10 at users.sourceforge.net> wrote: >> >> >> >> ------------------------------ >> On Sat, May 16, 2015 8:04 AM BST Uwe Ligges wrote: >> >> Not sure why this goes to R-devel. You just could have asked

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that

I am working on a competing risks problem, specifically an analysis of cause-specific mortality. I am familiar with the cmprsk package and have used it before to create cumulative incidence plots. I also came across an old (1998) s-news post from Dr. Terry Therneau describing a way to use coxph to model competing risks. I am re-producing the post at the bottom of this message. I would like to

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function

This message was also sent to the MEDSTATS mailing list, so here is the reply I posted to that: Philippe, The machinery to use is to split follow-up time so finely that you can safely assume that rates are constant in each interval, and then just stuff it all into a Poisson model. This allows you to use any kind of time-dependent variables as well as accommodating competing risks. In the Epi

I am trying to use the crr function in the cmprsk package to analyze a large patient dataset (45000 +), The model has 100 + covariates and 5 competing risks. I am finding that R seems to get bogged down and even if I let it run for several hours I don't get anything back. Am I expecting too much, or are there ways to speed up the process? Any help is appreciated. Best, Spencer