similar to: how to test the equalness of several coefficients in a gamma frailty model using R

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

Hello, I want to generate data set from Cox PH model with gamma frailty effects. theta(parameter for frailty distribution)=2 beta=1.5 n=300 cluster size=30 number of clusters=10 I think I should first generate u from Gamma(Theta,theta) and then using this theta I could not decide how I should generate the survival times? Is there any package for this? or any document you could suggest? Any

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).

Dear all, I'm new to frailty model. I have a question on the output from 'survival' pack. Below is the output. What does gamma1,2,3 refer to? How do I calculate joint hazard function or marginal hazard function using info below? Many thanks! Call: coxph(formula = surv ~ as.factor(tibia) + frailty(as.factor(bdcat)), data = try) n=877 (1 observation deleted due to missingness)

-------- Original Message -------- Subject: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model? Date: Mon, 04 Nov 2013 17:27:04 -0600 From: Terry Therneau <therneau.terry at mayo.edu> To: Y <yuhanusa at gmail.com> The cumulative hazard is just -log(sfit$surv). The hazard is essentially a density estimate, and that is much harder. You'll notice

Dear all, I have a data set<http://yaap.it/paste/c11b9fdcfd68d02b#gIVtLrrme3MaiQd9hHy1zcTjRq7VsVQ8eAZ2fol1lUc=>with 6 clusters, each containing 48 (possibly censored, in which case "event = 0") survival times. The "x" column contains a binary explanatory variable. I try to describe that data with a gamma frailty model as follows: library(survival) mod <-

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

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

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: >

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

Dear all, this is probably a very silly question, but could anyone tell me what the different parameters in a coxph model with a frailty.gamma term mean? Specifically I have two questions: (1) Compared to a "normal" coxph model, it seems that I obtain two standard errors [se(coef) and se2]. What is the difference between those? (2) Again compared to a "normal" coxph model,

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

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'm doing a cox regression with frailty: model <- coxph(Surv(Start,Stop,Terminated)~ X + frailty(id),table) I understand that model$frail returns the group level frailty terms. Does this mean this is the average of the frailty values for the respective groups? Also, if I'm fitting it to a gamma frailty, how do I extract the rate and scale parameters for the different gamma

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, 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

The combination of survreg + gamma frailty = invalid model, i.e., the example that you quote. I did not realize that this had been added to the survreg help file until very recently. I will try to fix the oversight. Other, more detailed documentation states that Gaussian frailty + AIC is the only valid random effects choice for survreg. Details: frailty(x) with no optional

SEE ALSO ORIGINAL POSTING IN PR#2933 On May 6, 2003 03:58 pm, Thomas Lumley wrote: > > Looking at a wider context in the code > > pfun <- function(coef, theta, ndeath) { > if (theta == 0) > list(recenter = 0, penalty = 0, flag = TRUE) > else { > recenter <- log(mean(exp(coef))) > coef <- coef - recenter

I would like to plot a survival curves of a group with different categories after running a Cox model with frailty() random effects terms. I just could display a survival plot of the covariable?s mean. Here an example: library(survival) fit<-coxph(Surv(time, status) ~ sex+ frailty(litter, dist='gamma', method='em'), rats) summary(fit ) suf<-survfit(fit) plot(suf,