search for: frailties

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

...2)) ) fit1 <- coxph(Surv(exit, event) ~ x + frailty(id, dist = "gaussian"), data = dat) return(fit$frail) } ----------------------------------------------- the result is 'NULL', but with 'real data', I usually get the predicted frailties. The help pages doesn't even mention the component 'frail', but in the code I can see that 'frail' is reurned if 'nfrail > 0'. And (from 'coxpenal.fit.s'): --------------------------- if (any(sparse)) { ... }else{ nfrail <- 0 ... } ----...

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

...'t understand the following: [1] The variance of the estimated individual frailty values is always much lower than both the original variance and the estimated variance of the random effect. Why is this ? Obviously the variance of the random effect is not calculated directly from the individual frailties after exponentiating them. [2] Random effects that are not very large are not even picked up by M2, e.g. theta = 10, yields a variance of ~1/10 in the frailty distribution used to set up the data, however, the estimated variance of that random effect equals only 0.003311. Why is frailty not picke...

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: >

...lem leads me to recommend to > use the negative log-likelihood of gamma variables (2). Hence, both gamma, > Gaussian and t frailty would be equivalent to maximum (partial) likelihood. > The log density penalty doesn't give maximum likelihood (which you would get by integrating out the frailties). It gives the joint likelihood of the data and the random effects. For the gamma model, as you note, they are equivalent. I believe that the current state of knowledge is that the log density penalty generally gives consistent estimates but is not equivalent to maximum likelihood. However, I hav...

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 =

...Iterations: 5 outer, 17 Newton-Raphson Variance of random effect= 3.37 ... It is not clear from the returned list how to get the printed 3.37. 2) What is the meaning of the variance of the random effects if we fit gamma frailities? For example: set.seed(1007) x <- runif(100) # gamma frailties m <- rgamma(10, shape = 1, scale = 2) # E(m) = 2, var(m) = 4 mu <- rep(log(m), rep(10,10)) test2 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution = "weibull"), Status = rep(1, 100), Unit = gl(10,10)...

...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 this as html earlier today. Chris Argyropoulos University of Pittsburgh Medical Center Background ------------- I am trying to fit a cox proportional hazards model tot...

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"), data = kidney)

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

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

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 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

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

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

...m effect in Hosmer & Lemeshow's Applied Survival Analysis (1999), pp.321-26 (I love your book, Dr. Therneay, but I needed something 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 frail...

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

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