similar to: Left truncation in shared frailty models with time-varying covariates

Dear list, I want o fit a shared gamma frailty model with the frailty specification in the survival package. I have partly left-truncated data and time-dependent covariates. Is it possible to combine these two things in the frailty function. Or are the results wrong if I use data in the start-stop-formulation which account for delayed entry? Is the frailty distribution updated in the

Hi, I would like to fit parametric survival models to time-to-event data that are left truncated. I have checked the help page for survreg and looked in the R-help archive, and it appears that the R function survreg from the survival library (version 2.16) should allow me to take account of left truncation. However, when I try the command

Dear Prof. Brostr?m, Dear R-mailinglist, first of all thanks a lot for your great effort to incorporate time-varying covariates into aftreg. It works like a charm so far and I'll update you with detailled benchmarks as soon as I have them. I have one more questions regarding Accelerated Failure Time models (with aftreg): You mention that left truncation in combination with time-varying

Hello, I'm trying to estimate a cox model with a frailty variable and time-dependent covariate (below there is the statement I use and the error message). It's seems to be impossible, because every time I add the time-dependent covariate the model doesn't converge. Instead, if I estimate the same model without the time-dependent covariate it's converge. I'd like knowing if

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

On Tue, 6 May 2003, Jerome Asselin wrote: > > I am confused on how the log-likelihood is calculated in a parametric > survival problem with frailty. I see a contradiction in the frailty() help > file vs. the source code of frailty.gamma(), frailty.gaussian() and > frailty.t(). > > The function frailty.gaussian() appears to calculate the penalty as the > negative

Dear R users: I'm generating the following survival data: set.seed(123) n=200 #sample size x=rbinom(n,size=1,prob=.5) #binomial treatment v=rgamma(n,shape=1,scale=1) #gamma frailty w=rweibull(n,shape=1,scale=1) #Weibull deviates b=-log(2) #treatment's slope t=exp( -x*b -log(v) + log(w) ) #failure times c=rep(1,n) #uncensored indicator id=seq(1:n) #individual frailty indicator

Hi R-users I spend a lot of time searching on the web but I didn?t found a clear answer. I have some doubts with 'frailty' function of 'survival' package. The following model with the function R ?coxph? was fitted: modx <- coxph(Surv(to_stroke, stroke) ~ age + sbp + dbp + sex + frailty(center,distribution = "gamma", method='aic'), data=datax) Then I get

Dear R-list users, I am trying to do simulation of survival data to enable it to run under frailty option. Below is the function a that I am using. My questions are: 1. How do I modify it to get bigger (hopefully significant) value of Variance of random effect? 2. What changes do I have to make in the function to run it under correlated frailty model? (may be in kinship package) 3. Is there

Dear R users, I'm trying to fit a gamma-frailty model on a simulated dataset, with 6 covariates, and I'm running into some results I do not understand. I constructed an example from my simulation code, where I fit a coxph model without frailty (M1) and with frailty (M2) on a number of data samples with a varying degree of heterogeneity (I'm running R 2.3.1, running takes ~1 min).

Hi everyone, I'm looking for any examples of useful frailty models, in particular any situation in which a cox proportional hazards model with frailty outperforms a regular cox proportional hazards model with respect to prediction of the time to event (or the X-year risk of an event). I have defined my own gamma-frailty cox PH model in R but on my simulated data sample it does not predict any

Dear all, Does the frailty.object$history[[1]]$theta returns the Variance of random effect? Why is the value different? Here is an example with kidney data: > library(survival) > data(kidney) > frailty.object<-coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney) > frailty.object Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id), data

Hi: I have a few clarification questions about the elements returned by the coxph function used in conjuction with a frailty term. I create the following group variable: group <- NULL group[id<50] <- 1 group[id>=50 & id<100] <- 2 group[id>=100 & id<150] <- 3 group[id>=150 & id<200] <- 4 group[id>=200 & id<250] <- 5 group[id>=250

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Does someone know the rules by which 'coxph' returns 'frail', the predicted frailty terms? In my test function: ----------------------------------------------- fr <- function(){ #testing(frailty terms in 'survival' require(survival) dat <- data.frame(exit = 1:6, event = rep(1, 6), x = rep(c(0, 1), 3),

Hi: I have a question about the use of the survfit function after the estimation of a cox proportional hazard model with a frailty term. My goal is to estimate expected survival probabilities while controlling for the group-specific frailty term. First, I estimate a model of the following form: model1 <- coxph(Surv(t0, t, d) ~ x1 + x2 + frailty(id), na.action=na.exclude,

Dear R-users, I have noticed small discrepencies in the reported estimate of the variance of the frailty by the print method for survreg() and the 'theta' component included in the object fit: # Examples in R-2.6.2 for Windows library(survival) # version 2.34-1 (2008-03-31) # discrepancy fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats) fit1 fit1$history[[1]]$theta