similar to: Competing risks - calibration curve

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 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

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

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

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 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 ?).

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 = ",",

Hello all, Can someone tell me why the following mixture of two log-normal distributions does not get truncated? What puzzles me is that the function works almost always, but for certain combinations (like the one below), it does not. # R code example library(distr) mix<-UnivarMixingDistribution(Lnorm(3.2,0.5),Lnorm(5.4,0.6),mixCoeff=c(0.3,0.7))

On Thu, Mar 22, 2018 at 4:52 AM, Raja, Dr. Edwin Amalraj <amalraj.raja at abdn.ac.uk> wrote: > Hi , > > library(foreign) > write.dta(data1, "data1.dta") > > should work. I don't think so: > library(foreign) > example(svydesign) > write.dta(dstrat, "~/Downloads/foo.dta") Error in write.dta(dstrat, "~/Downloads/foo.dta") : The

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-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Dear Thomas, I want to use evaluate effect of Age using restricted cubic form in the FGR function as Fgr.crr <- FGR(Hist(time, event) ~ rcs(Age_years), data=dat) It provides error. " Error in parse(text = termtext, keep.source = FALSE): .... 1: response ~ rcs(Age_years Do I need to change any of the R code? Regards Amalraj Raja The University of Aberdeen is a charity

Dear all, Happy New year! I have used the 'crr' function to fit the 'proportional subdistribution hazards' regression model described in Fine and Gray (1999). dat1 is a three column dataset where: - ccr is the time to event variable - Crcens is an indicator variable equal to 0 if the event was achieved, 1 if the event wasn't acheived due to death or 2 if the event wasn't

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

Hi , library(foreign) write.dta(data1, "data1.dta") should work. The file will be saved in the working directory. Use getwd() to know the working directory. Best wishes Amalraj Raja -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of rosario scandurra Sent: 22 March 2018 07:47 To: r-help at r-project.org Subject: [R] exporting data to stata

Dear all, I would like to use the following function fitdist(data, distr, method=c("mle", "mme", "qme", "mge"), start=NULL, fix.arg=NULL, ...) for many different distr values like distr=c("norm","lnorm","pois") (just a small example) and take back into a list the parameter name which is what is inside distr plus what the

I apologise as I had mistakenly posted this message via non- member mail. So I'm reposting it with member id. I need help in this case. > Hi friends, > I haven't done such a simulation before and any help would be greatly appreciated. I need your guidance. > > I need to simulate end to end data for Reliability/survival analysis of a Pump ,with correlation in place, that is

Hi friends, I haven't done such a simulation before and any help would be greatly appreciated. I need your guidance. I need to simulate end to end data for Reliability/survival analysis of a Pump ,with correlation in place, that is at 'Transactional level' or at the granularity of time-minutes, where each observation is a reading captured via Pump's sensors each minute. Once

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

'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