Displaying 20 results from an estimated 900 matches similar to: "Nested frailty model"
2007 Apr 20
1
Approaches of Frailty estimation: coxme vs coxph(...frailty(id, dist='gauss'))
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
2007 Sep 12
1
enquiry
Dear R-help,
I am trying to estimate a Cox model with nested effects basing on the
minimization of the overall AIC; I have two frailties terms, both gamma
distributed. There is a error message (theta2 argument misses) and I
don?t understand why. I would like to know what I have wrong. Thank you
very much for your time.
fitM7 <- coxph(Surv(lifespan,censured) ~ south + frailty(id,
2011 Jun 25
2
cluster() or frailty() in coxph
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 =
2012 Feb 03
1
coxme with frailty--variance of random effect?
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), #
2012 Dec 03
1
fitting a gamma frailty model (coxph)
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 <-
2007 Dec 05
4
coxme frailty model standard errors?
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
2004 Nov 08
1
coxph models with 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
2002 Oct 08
2
Frailty and coxph
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),
2003 Aug 04
1
coxph and frailty
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
2003 May 19
1
survit function and cox model with frailty
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,
2005 May 31
1
Shared Frailty in survival package (left truncation, time-dep. 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
2008 Apr 17
1
survreg() with frailty
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
2005 Sep 08
1
Survival model with cross-classified shared frailties
Dear All,
The "coxph" function in the "survival" package allows multiple frailty
terms. In all the examples I saw, however, the frailty terms are nested.
What will happen if I have non-nested (that is, cross-classified) frailties
in the model? Will the model still work? Do I need to take special cares
when specifying these models? Thanks!
Shige
[[alternative HTML
2009 Aug 31
2
How to extract the theta values from coxph frailty models
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
2011 Dec 30
2
Joint modelling of survival data
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 =
2004 Nov 17
1
frailty and time-dependent covariate
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
2005 Jul 21
1
output of variance estimate of random effect from a gamma frailty model using Coxph in 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
2004 Nov 24
1
OOT: frailty-multinivel
Hola!
I started to search for information about multilevel survival models, and
found frailty in R. This seems to be something of the same, is it the same?
Then: why the name frailty (weekness?)
--
Kjetil Halvorsen.
Peace is the most effective weapon of mass construction.
-- Mahdi Elmandjra
2013 Oct 09
1
frailtypack
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
2007 Apr 17
3
Extracting approximate Wald test (Chisq) from coxph(..frailty)
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)