search for: frailty

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 group=rep(1:(n/10), 10) #shared frailty indicator Then I'm using the survival packag...

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),...

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 approach too? coxph(...frailty(id, dist='gamma')) estimates by means of the pena...

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...

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: > frailty.gaussi...

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 log-density of independent Gaussian variables, as one would > ex...

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 Cal...

...m(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution = "weibull"), Status = rep(1, 100), Unit = gl(10,10) ) mod1 <- survreg(Surv(Time, Status) ~ 1 + frailty.gaussian(Unit), data = test1) > mod1 ... coef se(coef) se2 Chisq DF p (Intercept) 0.987 0.582 0.0457 2.87 1.00 9.0e-02 frailty.gaussian(Unit) 85.26 8.95 1.4e-14 Scale= 0.434 Iterations: 5 outer, 17 Newton-Raphson Var...

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 appers in the frailty term ? Thanks in advance! I apologise for sending th...

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss&quo...

Hi, I have a question about the output for variance of random effect from a gamma frailty model using coxph in R. Is it the vairance of frailties themselves or variance of log frailties? Thanks. Guanghui

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 survival (eg to 10 years) fo...

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 & id<300] <- 6 gr...

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, data=My.data) Then, I prepare a...

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?...

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 a...

Hello, I am working on the frailty model using coxph functions. I am running some simulations and want to store the variance of frailty (theta) values from each simulation result. Can anyone help me how to extract the theta values from the results. I appreciate any help. Thanks Shankar Viswanathan

...ng easier...). They proceed in 4 steps: 1. Obtain the cumulative hazard function for each subject. 2. Choose an arbitrary value for the variance parameter of the frailties (call it theta). 3. Compute for each subject an estimate of the value of their frailties, USING this variance parameter theta: frailty_i= \frac(1+\theta \times c_i}{1+\theta \times H_i} (formula on p. 321), where H is the cumulative hazard for the subject. So if theta is 0 (no variance), then frailty=1 (i.e., no excess frailty). As theta goes to infinity, the estimated frailty is simply the ratio 1/(cumulative risk so far) or 1/(c...

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 # OK...

Dear R-help, I am trying to estimate a Cox model with nested effects, or better h(t,v,w)=v*w*h0(t)*exp(B'x) where h(t,v,w) is the individual hazard function w and v are both frailty terms (gamma or normal distributed) I have 12 clusters and for each one of them I would like to associate a realization of v, while w is a random effect for the whole population. At the population level I should integrate out both the frailty distributions. This type of model is usually called n...