similar to: Paired data survival analysis

Hola Estoy tratando de correr un survival analysis usando un Cox regression model. Tengo una duda respecto a la organizacion del script. Tengo una variable que es -tamano del individuo- y quiero ver si hay diferencia en sobrevivencia respecto a tamano. Como diseno de campo los tamanos fueron ubicados de forma aleatoria en bloques al azar. Cuado planteo el script tengo algo como:

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

Hello, We are using frailty models to estimate risk of one year death. Is there a way to generate survival curves adjusted for covariates and also include frailty term? Any help will be much appreciated! Thanks! LV [[alternative HTML version deleted]]

Dear All, The "coxph" function in the "survival" package allows multiple frailty terms. In all the examples I saw, however, the frailty terms are nested. What will happen if I have non-nested (that is, cross-classified) frailties in the model? Will the model still work? Do I need to take special cares when specifying these models? Thanks! Shige [[alternative HTML

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

Dear R-Helpers, I am hoping to perform survival analyses using the "ex-Gaussian" distribution. I understand that the ex-Gaussian is a convolution of exponential and Gaussian distributions for survival data. I checked the "survreg.distributions" help and saw that it is possible to mix pre-defined distributions. Am I correct to think that the following code makes the

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

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

I have a large excel file with data in it. I converted it to a 'csv' format. I imported this dataset to R using the follownig command mldata <- read.csv("c:\\temp\\mldata.csv", header=T) all the column names and the rows seems to be correct. Now that I have this object, I need to perfrom hclust. I used the following hc <- hclust(dist(mldata), method="single")

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

Hey list, I'm having problems to get running *. I don't have any digium hardware yet. I just want to perfrom some tests using SIP. I compiled asterisk and zaptel with ztdummy enabled on Fedora Core 3. When I try to start ztdummy I get the following message: localhost# modprobe ztdummy Notice: Configuration file is /etc/zaptel.conf line0: Unable to open master device /dev/zap/ctl 1

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 =

Hi All, I am currently conducting some survival analyses. I would like to extract coefficients at each level of the IVs. I read on a previous posting that dummy regression using coxph was not possible. Therefore I though, hey why not categorize the variables (I realize some folks object to categorization but the paper I am replicating appears to have done so ...) and turn the variables

The latest version of the survival package has two important additions. In prior code the call coxph(Surv(time, status) ~ age + strata(inst), data=lung) could fail if a version of either Surv() or strata() existed elsewhere on the search path; the wrong function could be picked up. Second, a model with survival::strata(inst) in the formula would not do what users expect. These

> Gesendet: Donnerstag, 27. Juni 2013 um 17:44 Uhr > OK, now that's just weird. You don't see any response whatsoever from > tapping at the keys? > > If you set a TIMEOUT, can you run a DEFAULT even though you cannot type? > That's one way to figure out whether the machine is dead at that point > or not. A useful DEFAULT might be reboot.c32, that way even if the

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

> 1. What is your output of testparm? No errors or warnings, apart from rlimit_max: increasing rlimit_max (1024) to minimum Windows limit (16384) > 2. If you run top, are any Samba related processes (winbindd, smbd, etc) consuming excessively high amounts of CPU? I did not observe this, although the machine was running at a load of 1+ with no apparent culprit. > 3. Have you

Parallels proprietary hypervisor uses RDPMC as the hypercall instruction. As this instruction is supported since early P6 family, the drivers didn't even bother to check for the presence of the corresponding feature in CPUID. In QEMU/KVM, however, this instruction triggers #GP unless the VM is run with PMU (performance monitoring unit) enabled, which is often not the case (due to its impact

Hi, I have a series of id's with multiple visits and questionnaire scores. This is a clinical trial that will be analyzed using the last observation carried forward method. In other words, in order to comply with intent to treat analysis when many subjects withdraw, data points for the last visit must be generated and filled in with the last observation. The ultimate goal is to tabulate the

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