similar to: competing risk model with time dependent covariates under R or Splus

There is now an Epi-package on CRAN. It is intended for epidemiological analysis in R. It has its own homepage, http://www.pubhealth.ku.dk/~bxc/Epi The package has been used at the course "Statistical practise in Epidemiology with R", see http://www.pubhealth.ku.dk/~bxc/SPE. A mailing list for people using R for epidemiology, R-sig-Epi, has been set up, see

There is now an Epi-package on CRAN. It is intended for epidemiological analysis in R. It has its own homepage, http://www.pubhealth.ku.dk/~bxc/Epi The package has been used at the course "Statistical practise in Epidemiology with R", see http://www.pubhealth.ku.dk/~bxc/SPE. A mailing list for people using R for epidemiology, R-sig-Epi, has been set up, see

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 = ",",

Course in STATISTICAL PRACTICE IN EPIDEMIOLOGY USING R ==================================================== Tartu, Estonia, 26 - 31 May 2005 The course is aimed at epidemiologists and statisticians who wish to use R for statistical modelling and analysis of epidemiological data. The course requires basic knowledge of epidemiological concepts and study types. These will only be briefly reviewed,

Dear R users, is there a way, I mean a package, to perform a competing risk model which can handle time dependent covariates ? my main covariate (additional treatment to patients) appears not to follow the proportional hazards assumption, its effect being observed after one year of treatment but not before (this is expected / makes sense on a clinical point of view). SO I was planning to use a

Hi All, I'm trying to figure out the cumulative incidence curve in R in some limited time. I found in package "cmprsk", the command "plot.cuminc" can get this curve. But I noticed that there is no mark for the censored time there, comparing with the KM curve by "plot.survfit". Here are my codes (attached is the data): ----------------

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 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 ?).

Hi, I am new to R and I'm looking to perform some method comparison analysis and would be grateful for some help regarding package installation. There is a package 'MethComp' that I want to use (http://staff.pubhealth.ku.dk/~bxc/MethComp/Courses/Sthm.2007/.) Unfortunately I am unable to install it. I have tried numerous approaches but to no avail. this is the first time I have tried to

A new major upgrade of the Epi package for Epidemiological data analysis has been put on CRAN, it is now at version 1.0.7. It contains an entirely new way of representing follow-up data on multiple timescales and multiple states. See the function Lexis(). Plus a lot of other useful stuff for epidemiological analysis. See more on the package homepage, www.biostat.ku.dk/~bxc/Epi Note also that

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

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

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

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)

Dear R users, I used crr function in R package 'cmprsk' to fit a competing risks model. There were no any error or warning messages during running the function, but the output was obvious not correct. I saved the model fit as an object called f.crr. I extracted bfitj from the object by doing f.crr$bfitj, and found values of bfitj were extremely large (around 1e+138). I tried all

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

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

Dear R-community, I would like to plot the numbers at risk for the different causes of failure at specific timepoints below a cumulative incidence function plot (plot.cuminc-function, cmprsk-package). For a Kaplan-Meier plot I know this is possible with the n.risk-argument in the survplot-function (rms-package), but to my knowledge no such readily-available functions are available for competing

I am updating the Epi package. I added functions named pc.points and pc.matpoints. Erroneously I wrote pc.plot and pc.matplot in the NAMESPACE file and of course got an error from Rcmd check. So I corrected the NAMESPACE file, but I still get from r-check: * install options are ' --no-html' Loading required package: utils Error in namespaceExport(ns, exports) : undefined exports:

Did not seem to reach Saikat DebRoy, this might be the forum. Bendix -----Original Message----- From: BXC (Bendix Carstensen) Sent: 24. november 2008 15:00 To: 'saikat at stat.wisc.edu'; 'stvjc at channing.harvard.edu' Subject: read.ssd It's always annoyed me that that read.ssd crashed on datasets with long variable names, but the other day a collegue of mine pointed out to