Displaying 20 results from an estimated 700 matches similar to: "Plot survival curves after coxph() with frailty() random effects terms"
2007 Jun 20
0
shoudl I use apply, sapply, etc instead of a "for loop"?
I have been trying to learn the various "apply" functions but am still learning their appropriate use. I appreciate any help the R community can offer me. Sorry for the length of this post.
Background:
I have data on my hard drive organized in the following manner:
The data pertains to many different "samples" of data. (e.g. sample 001, sample, 002, sample 003, etc.)
Each
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
2002 Dec 01
1
generating contrast names
Dear R-devel list members,
I'd like to suggest a more flexible procedure for generating contrast
names. I apologise for a relatively long message -- I want my proposal to
be clear.
I've never liked the current approach. For example, the names generated by
contr.treatment paste factor to level names with no separation between the
two; contr.sum simply numbers contrasts (I recall an
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
2003 May 07
0
frailty models in survreg() -- survival package (PR#2933)
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:
>
2009 Jan 07
0
Frailty by strata interactions in coxph (or coxme)?
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
2006 Sep 05
1
help: advice on the structuring of ReML models for analysing growth curves
Hi R experts,
I am interested on the effects of two dietry compunds on the growth of
chicks. Rather than extracting linear growth functions for each chick and
using these in an analysis I thought using ReML might provide a neater and
better way of doing this. (I have read the pdf vignette("MlmSoftRev") and
"Fitting linear mixed models in R" by Douglas Bates but I am not
2011 Apr 05
0
frailty
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
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
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
2007 Apr 08
0
Simulation of the Frailty of the Cox PH model
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
2003 May 07
0
Re: frailty models in survreg() -- survival package (PR#2934)
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
2008 Feb 21
2
Nested frailty model
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
2006 Sep 21
0
Any examples of a frailty model actually used for prediction ?
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
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
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
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
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 =