similar to: predicted values of coxme model

Hello, I have a question concerning fitting a cox model with a random intercept, also known as a frailty model. I am using both the coxme package, and the frailty statement in coxph. Often 'shared' frailty models are implemented in practice, to group people who are from a cluster to account for homogeneity in outcomes for people from the same cluster. I am more interested in the classic

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

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

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) >

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), #

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)

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

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

Hi all, I am using the coxme function to fit random effects survival models. The base survival models show a typical "J" shaped curve where survival drops sharply then plateaus over time interval. I want to include a time covariate which could account for this non-linear decrease in survival over time. However, I have not found a clear way to do this in the coxme models. Any

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

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

Hello, and thanks for your time I'm trying to extract standard errors to produce confidence intervals from a multivariable coxme model object so I can write a function that will print a summary for some reproducible research. As far as I can glean, the SE is produced on-the-fly by the print method. I'll dig into the source code if I have to, but I'd rather not have to. Any help

Dear R community I currently try to get post hoc multiple comparisons with the package multcomp from a cox mixed-effects model, where the survival is explained by two variables (cover with levels: nocover and cover; treatment with levels: tx, uv, meta), whose interaction is significant. I read Hothorn, T. 2011: Additional multcomp Examples and there is an example involving a two-way ANOVA with

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

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major

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

I am a relative newbie to survival analysis and R in general, but would like to use the coxme package to analyse some data I currently have. The data is relative to survival times of drosophila melanogaster populations to infection with pathogens, and has the variables: Time, Status, Treatment (4 treatments + 2 controls) Population Replicate ?and I'm currently using the following call

------------- 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

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)) >

Hi, R experts I am currently using lmekin() function in coxme package to fit a mixed effect model for family based genetic data. How can I extract the p value from a lmekin object? When I print the object in R console, I can see the p value and Z value are just over there. But I can not extract them by the coef() function. kinfit$coefficient$fixed (kinfit is the name of the lmekin object)