Displaying 20 results from an estimated 8000 matches similar to: "How to extract the theta values from coxph frailty models"
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 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 <-
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
2006 Sep 22
0
$theta of frailty in coxph
Dear all,
Does the frailty.object$history[[1]]$theta returns the Variance of random
effect?
Why is the value different? Here is an example with kidney data:
> library(survival)
> data(kidney)
> frailty.object<-coxph(Surv(time, status)~ age + sex + disease +
frailty(id), kidney)
> frailty.object
Call:
coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id),
data
2008 Apr 17
1
survreg() with frailty
Dear R-users,
I have noticed small discrepencies in the reported estimate of the
variance of the frailty by the print method for survreg() and the
'theta' component included in the object fit:
# Examples in R-2.6.2 for Windows
library(survival) # version 2.34-1 (2008-03-31)
# discrepancy
fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats)
fit1
fit1$history[[1]]$theta
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
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 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)
2010 Apr 26
1
Interpreting output of coxph with frailty.gamma
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,
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
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,
2009 Jun 24
1
Coxph frailty model counting process error X matrix deemed singular
Hello,
I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning:
Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, :
X matrix deemed to be singular; variable 2
My
2006 Sep 21
0
frailty in coxph
Dear all,
I have been doing some frailty calculations and been facing some
difficulties.
I can extract coefficients, value of theta and the following things
library(survival)
fit<-coxph(Surv(time,status)~covariate+frailty(group), data=simulated.data)
fit$coef
fit$history[[1]]$theta
fit$history[[1]]$c.loglik
fit$var
fit$var2
from a frailty included coxph object:
but how can i know what other
2005 Sep 07
1
Survival analysis with COXPH
Dear all,
I would have some questions on the coxph function for survival analysis,
which I use with frailty terms.
My model is:
mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'),
data)
I have a very large proportion of censored observations.
- If I understand correctly, the function mdcox$frail will return the random
effect estimated for each group on the
2009 Feb 23
1
predicting cumulative hazard for coxph using predict
Hi
I am estimating the following coxph function with stratification and frailty?where each person had multiple events.
m<-coxph(Surv(dtime1,status1)~gender+cage+uplf+strata(enum)+frailty(id),xmodel)
?
> head(xmodel)
id enum dtime status gender cage uplf
1 1008666 1 2259.1412037 1 MA 0.000 0
2 1008666 2 36.7495023 1 MA 2259.141 0
3 1008666
2004 Nov 17
1
frailty and time-dependent covariate
Hello,
I'm trying to estimate a cox model with a frailty variable and time-dependent covariate (below there is the statement I use and the error message). It's seems to be impossible, because every time I add the time-dependent covariate the model doesn't converge. Instead, if I estimate the same model without the time-dependent covariate it's converge. I'd like knowing if
2003 May 19
1
survit function and cox model with frailty
Hi:
I have a question about the use of the survfit function after the
estimation of a cox proportional hazard model with a frailty term. My goal
is to estimate expected survival probabilities while controlling for the
group-specific frailty term.
First, I estimate a model of the following form:
model1 <- coxph(Surv(t0, t, d) ~ x1 + x2 + frailty(id), na.action=na.exclude,
2010 Feb 16
1
survival - ratio likelihood for ridge coxph()
It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected?
In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.
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), #