Displaying 20 results from an estimated 2000 matches similar to: "Fwd: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model?"
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
2004 Jul 04
2
smooth non cumulative baseline hazard in Cox model
Hi everyone.
There's been several threads on baseline hazard in Cox model but I think
they were all on cumulative baseline hazard,
for instance
http://tolstoy.newcastle.edu.au/R/help/01a/0464.html
http://tolstoy.newcastle.edu.au/R/help/01a/0436.html
"basehaz" in package survival seems to do a cumulative hazard.
extract from the basehaz function:
sfit <- survfit(fit)
H
2006 Sep 19
0
How to interpret these results from a simple gamma-frailty model
Dear R users,
I'm trying to fit a gamma-frailty model on a simulated dataset, with 6 covariates, and I'm running into some results I do not understand. I constructed an example from my simulation code, where I fit a coxph model without frailty (M1) and with frailty (M2) on a number of data samples with a varying degree of heterogeneity (I'm running R 2.3.1, running takes ~1 min).
2011 Sep 02
1
Parameters in Gamma Frailty model
Dear all,
I'm new to frailty model. I have a question on the output from 'survival'
pack. Below is the output. What does gamma1,2,3 refer to? How do I
calculate joint hazard function or marginal hazard function using info
below? Many thanks!
Call:
coxph(formula = surv ~ as.factor(tibia) + frailty(as.factor(bdcat)),
data = try)
n=877 (1 observation deleted due to missingness)
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
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 <-
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,
2009 Jun 16
0
Generation from COX PH with gamma frailty
Hello,
I want to generate data set from Cox PH model with gamma frailty effects.
theta(parameter for frailty distribution)=2
beta=1.5
n=300
cluster size=30
number of clusters=10
I think I should first generate u from Gamma(Theta,theta) and then using
this theta I could not decide how I should generate the survival times?
Is there any package for this? or any document you could suggest?
Any
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
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
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:
>
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
2003 May 07
0
Re: frailty models in survreg() -- survival package (PR#2934)
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
2005 Jul 22
0
how to test the equalness of several coefficients in a gamma frailty model using R
Hi,
I want to test the equalness of several coefficients of a gamma frailty model
using R. In SAS, a TEST statement can be used for a cox model.How to do it in
R?
Thanks a lot!
Guanghui
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
2008 Apr 18
0
survreg with frailty
The combination of survreg + gamma frailty = invalid model, i.e., the example
that you quote.
I did not realize that this had been added to the survreg help file until very
recently. I will try to fix the oversight. Other, more detailed documentation
states that Gaussian frailty + AIC is the only valid random effects choice for
survreg.
Details: frailty(x) with no optional
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
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
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
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