similar to: Changes in the survival package (long)

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

Martin, ? There are a couple of issues with [.terms that have bitten my survival code.? At the useR conference I promised you a detailed (readable) explanation, and have been lax in getting it to you. The error was first pointed out in a bugzilla note from 2016, by the way.? The current survival code works around these. Consider the following formula: <<testform>>=

On 24/02/2020 8:55 a.m., Therneau, Terry M., Ph.D. via R-devel wrote: > I recently had a long argument wrt the survival package, namely that the following code > didn't do what they expected, and so they reported it as a bug > > ? survival::coxph( survival::Surv(time, status) ~ age + sex + survival::strata(inst), > data=lung) > > a. The Google R style guide? recommends

I got good feedback from the list about a scope issue, so I am coming back for more. Prior issue: users who type survival::coxph(survival::Surv(time, status) ~ x1 + x2 + surv ival::strata(group), data=mydata) This messes up the character string matching for strata, done via tt <- terms(formula, specials= ?strata?). The code runs, and gives the wrong answer (group is treated as an ordinary

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

In the long run, coming up with a way to parse specials in formulas that is both clean and robust is a good idea - annoying users are a little bit like CRAN maintainers in this respect. I think I would probably do this by testing identical(eval(extracted_head), survival::Surv) - but this has lots of potential annoyances (what if extracted_head is a symbol that can't be found in any attached

-------- Original Message -------- Subject: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model? Date: Mon, 04 Nov 2013 17:27:04 -0600 From: Terry Therneau <therneau.terry at mayo.edu> To: Y <yuhanusa at gmail.com> The cumulative hazard is just -log(sfit$surv). The hazard is essentially a density estimate, and that is much harder. You'll notice

Hi, I would like to fit parametric survival models to time-to-event data that are left truncated. I have checked the help page for survreg and looked in the R-help archive, and it appears that the R function survreg from the survival library (version 2.16) should allow me to take account of left truncation. However, when I try the command

First, here is your message as it appears on R-help. On 10/14/2012 05:00 AM, r-help-request@r-project.org wrote: > I?m trying to set up proportional hazard model that is stratified with > respect to covariate 1 and has an interaction between covariate 1 and > another variable, covariate 2. Both variables are categorical. In the > following, I try to illustrate the two problems that

[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)

Dear list members, I am working with a Cox ph model for the duration of unemployment. The event of interest in my analysis is getting employed. I have various background variables explaining this event: age, sex, education etc. I have multiple unemployment spells per person. I use a model with person-specific frailty terms in order to take into account the correlation of spells by the same

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

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

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

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

I am not sure if this is the right place for that kind of questions, but I wondered that the recommended package survival did not pass R's check procedure without warnings: 1) unbalanced braces: * Rd files with unbalanced braces: * man/Surv.Rd * man/cluster.Rd * man/cox.zph.Rd * man/coxph.Rd * man/coxph.detail.Rd * man/date.ddmmmyy.Rd * man/lines.survfit.Rd *

I would like to plot a survival curves of a group with different categories after running a Cox model with frailty() random effects terms. I just could display a survival plot of the covariable?s mean. Here an example: library(survival) fit<-coxph(Surv(time, status) ~ sex+ frailty(litter, dist='gamma', method='em'), rats) summary(fit ) suf<-survfit(fit) plot(suf,

Dear useRs, What is the syntax to obtain survival curves for single strata on many subjects? I have a model based on Surv(time,response) object, so there is a single row per subject and no start,stop and no switching of strata. The newdata has many subjects and each subject has a strata and the survival based on the subject risk and the subject strata is needed. If I do newpred <-

Hello R users,  I am trying to obtain a direct adjusted survival curve. I am sending my whole code (see below). It's basically the larynx cancer data with Stage 1-4. I am using the cox model using coxph option, see the fit3 coxph. When I use the avg.surv option on fit3, I get the following error: "fits<-avg.surv(fit3, var.name="stage.fac", var.values=c(1,2,3,4), data=larynx)

Well, I would suggest using the code already in place in the survival package. Here is my code for your problem. I'm using a copy of the larynx data as found from the web resources for the Klein and Moeschberger book. larynx <- read.table("larynx.dat", skip=12, col.names=c("stage", "time", "age", "year",