Displaying 20 results from an estimated 1000 matches similar to: "coxme in R underestimates variance of random effect, when random effect is on observation level"
2018 Apr 04
1
parfm unable to fit models when hazard rate is small
Hello, I would like to use the parfm package: https://cran.r-project.org/web/packages/parfm/parfm.pdfhttps://cran.r-project.org/web/packages/parfm/parfm.pdf in my work. This package fits parametric frailty models to survival data. To ensure I was using it properly, I started by running some small simulations to generate some survival data (without any random effects), and analyse the data using
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), #
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
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
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
2007 Apr 20
1
Hiding "Warning messages" in coxme output
Dear list,
I have been trying to use coxme in R 2.3.1.
When I use coxme in the following data sim.fr1, i get
"Warning messages: using 'as.environment(NULL)' is
deprecated"
Why does it occur?
How can I hide such warning message,
especially when coxme is under a loop?
Mohammad Ehsanul Karim (Institute of Statistical
Research and Training, University of Dhaka)
>
2012 Feb 10
0
coxme with frailty
A couple of clarifications for you.
1. I write mixed effects Cox models as exp(X beta + Z b), beta = fixed
effects coefficients and b = random effects coefficients. I'm using
notation that is common in linear mixed effects models (on purpose).
About 2/3 of the papers use exp(X beta)* c, i.e., pull the random
effects out of the exponent. Does it make a difference? Not much: b
will be
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
2011 Jul 27
0
: Re: coxme frailty model standard errors?
-- begin included message --
Hi, but why we do the difference : ltemp <- 2 * diff(tfit
$loglik[1:2]) ??
Where I can find information about Integrate Likelihooh and null like
lihood??
--- end inclusion ---
1. Basic statistical fact: 2 * difference in loglik between two nested
models = distributed as a chi-square distribution. For coxme loglik[1]
= likelihood from a null model (all coefs
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 =
2011 Jul 08
1
coxme for random effects only model
Dear all,
I have encountered the following problem where coxme seems to allow
model with only random effect in R 2.11.1 but not in R 2.13.0. Following
is the error message using rat example data. Any comment on this is
appreciated.
In R2.13
> library(coxme)
> rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats)
> rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)
2012 Apr 16
0
warning message: coxme with package multcomp
Hi
I'm encountering an error/warning when doing multiple comparisons with
the package multcomp on a coxme model.
My data:
I'm looking at the removal of brood from the nest according to three
treatments I applied on the brood.
The brood and the workers caring about the brood in the nest, belonged
to different colonies.
Factor: treatment (3 levels: tx,uv,meta)
Random effect 1: origin of
2013 Apr 16
0
Model ranking (AICc, BIC, QIC) with coxme regression
Hi,
I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below).
#Model1
>spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring)
I've already found some explications in
2006 Jul 05
0
Problem with coxme
------------- Begin Forwarded Message -------------
Date: Wed, 5 Jul 2006 09:09:14 -0500 (CDT)
From: Terry Therneau <therneau at mayo.edu>
Subject: RE: Problem with coxme
To: jhz22 at medschl.cam.ac.uk
Cc: R-help at stat.mat.ethz.ch, liulei at virginia.edu, spencer.graves at pdf.com
Content-MD5: BXKVsHtW/1I9mIUqrXBU0g==
The original question involved a strange error message from coxme
2012 Feb 19
1
coxme: model simplification using LR-test?
Hi
I'm encountering some problems with coxme
My data:
I'm looking at the survival of animals in an experiment with 3 treatments,
which came from 4 different populations, two of which were infected with a
parasite and two of which were not. I'm interested if infected animals
differe from uninfected ones across treatments.
Factor 1: treatment (3 levels)
Factor 2: infection state
2011 Jan 25
1
coxme and random factors
Hi
I would really appreciate some help with my code for coxme...
My data set
I'm interested in survival of animals after an experiment with 4
treatments, which was performed on males and females. I also have two
random factors:
Response variable: survival (death)
Factor 1: treatment (4 levels)
Factor 2: sex (male / female)
Random effects 1: person nested within day (2 people did
2008 Dec 28
1
Random coefficients model with a covariate: coxme function
Dear R users:
I'm new to R and am trying to fit a mixed model
Cox regression model with coxme function.
I have one two-level factor (treat) and one
covariate (covar) and 32 different groups
(centers). I'd like to fit a random coefficients model, with treat and covar
as fixed factors and a random intercept, random
treat effect and random covar slope per center.
I haver a couple of
2008 Mar 05
1
coxme - fitting random treatment effect nested within centre
Dear all,
I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error.
> ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/')
> mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup))
>
2010 Aug 22
2
coxme AIC score and p-value mismatch??
Hi,
I am new to R and AIC scores but what I get from coxme seems wrong. The AIC
score increases as p-values decrease.
Since lower AIC scores mean better models and lower p-values mean stronger
effects or differences then shouldn't they change in the same direction? I
found this happens with the data set rats as well as my own data. Below is
the output for two models constructed with the rats
2011 Dec 30
0
New version of coxme / lmekin
Version 2.2 of coxme has been posted to CRAN, Windows versions and
mirrors should appear in due course. This is a major update with three
features of note:
1. A non-upwardly compatable change:
Extractor functions: beta= fixed effects, b=random effects
nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2
------------------------------------------------------
beta