similar to: Competings risks

Dear Raja, A Fine and Gray model can be fitted using the standard coxph function with weights that correct for right censoring and left truncation. Hence I guess any function that allows to perform stepwise regression with coxph should work. See e.g. my article in Biometrics https://doi.org/10.1111/j.1541-0420.2010.01420.x, or the vignette "Multi-state models and competing risks" in the

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 Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is

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

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:

Hi, Sorry not to provide R-code in my previous mail. R code is below #install.packages("rms") require(rms) #install.packages("mstate") library(mstate) require(splines) library(ggplot2) library(survival) library(splines) #install.packages("survsim") require(survsim) set.seed(10) df<-crisk.sim(n=500, foltime=10, dist.ev=rep("lnorm",2),

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 group: I am compiling a list of available algorithms/macros/software for performing the following types of competing risks analyses: (1) cumulative (crude) incidence regression (2) Gray's K-sample log-rank test for cmulative incidence (3) multi-state models (4) dependent competing risks modeling with (a) copulas, (b) Robin's Inverse probability of censoring weighted (IPCW)

Dear R users, I am new to R and wanted to apply competing risk methods in my research work. I used the R code given by Zhang et al in his paper 'Nomogram for survival analysis in the presence of competing risks published in Ann Trans Med 2017:5(20):403. I am struggling with getting calibration curve thro' internal validation. I am happy to receive suggestion in the coding as well

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 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

Hi Rs, I am analyzing a time to event dataset with several competing risks. 0 = Active by end of study 1 = Stopped treatment to start another treatment 2 = Lost 3 = Dead My event of interest in Lost to Followup but starting a different treatment and dying are competing risks. All 1,2,3 events are events of exiting the study, but it's only 2-LTFU that we are concerned with (I know I am

How can I model coxph() in combination with competing risks i.e. I have two events and for event the object will leave the data set. So : Coxph(Surv(time,event)~....) the event is for all my objects 1. How can I model this? Sharon ----------------------------------------------------------------- ATTENTION: The information in this electronic mail message is private and confidential, and only

Dear All, I would like to use R function 'selectFGR' of fine gray model in competing risks model. I used the 'Melanoma' data in 'riskRegression' package. Some of the variables are factor. I get solution for full model but not in variable selection model. Any advice how to use factor variable in 'selectFGR' function. The following R code is produced below

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

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

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

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

Dear R users: Analysis of the impact of a time-dependent covariate (GVHD or use of steroid after bone marrow transplantation) on two competing endpoints (invasive fungal infection and death) is frequently encountered in the setting of BMT data. Coxph package can be used as the following: for the analysis of GVHD: > gvhd -> coxph(Surv(start,stop,status = =1) ~ GVHD, data=bmt.data)

Hola Klaus y Jorge , gracias por responder. Intente eso mismo y me borra la columna "y", yo quiero eliminar la columna que se genera en los data frame que contiene el número de registro. En el ejemplo de abajo esa columna va de 1 a 4, cuando exporta el data frame se genera esa misma columna (que no tiene cabecera) Años Edad Dpto 1 Bueno 16,75 1 2 Bueno 16 1 3 Malo