similar to: new version of FRAILTYPACK: general frailty models

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

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

A new version of the package FRAILTYPACK is now available on CRAN. -- possibility to fit now a Shared and a Joint Frailty model with a log-normal distribution for the random effects. -- possibility to deal with interval-censored data (for a shared frailty model) -- possibility to fit a joint frailty model for clustered data For more details see the corresponding NEWS files in the pkgs. We are

A new version of the package FRAILTYPACK is now available on CRAN. -- possibility to fit now a Shared and a Joint Frailty model with a log-normal distribution for the random effects. -- possibility to deal with interval-censored data (for a shared frailty model) -- possibility to fit a joint frailty model for clustered data For more details see the corresponding NEWS files in the pkgs. We are

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]

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

Dear, I want to analyze two-level survival data using a shared frailty model, for which I want to use the R package 'Frailtypack", proposed by Rondeau et al. The dataset was built using SAS software. I also tried to change the format using SPSS and Excell. My (reduced) dataset has following column names: ID entry time status family var1 I used following command: >

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

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

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

Hello, I am running R 2.6.1 on windows xp I am trying to fit a cox proportional hazard model with a shared Gaussian frailty term using coxme My model is specified as: nofit1<-coxme(Surv(Age,cen1new)~ Sex+bo2+bo3,random=~1|isl,data=mydat) With x1-x3 being dummy variables, and isl being the community level variable with 4 levels. Does anyone know if there is a way to get the standard error

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

Hi! I want to estimate a shared gamma frailty model with left truncated data. I use a parametric baseline hazard so that I can use simple ML estimation. As I have a big data set it is ok to assume piecewise constant baseline hazards. As my data are left truncated I have modified the definition of the risk set. Do I also have to modifiy the frailty distribution if I have left truncated data?

Hi, when i run the following code i get this massege: "The instruction at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart,

Dear R-listers, I have a very strange problem. I made a package (under Windows and Linux). The package passed the R CMD Check without problem. Then, I installed the package and executed a function which calls to a 'dll' mod<-frailtyPenal(Surv(time,status)~sex+age+cluster(id), + n.knots=8,kappa1=10000,data=kidney) mod Call: frailtyPenal(formula = Surv(time, status)

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

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

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,