similar to: A question on time-dependent covariates in the Cox model.

Dear All,   I made some headways on my ODE problem in R2OpenBUGS, but got stuck again. Wonder if someone could help on the following:   I am running the following code:   library(R2OpenBUGS) dosetotal <-c(500,250) z <-c(4,2) grid <-c(4,15) n.grid <-2 tol <-0.001 dim <-2 T <-2 origin <-0 dosetime <-c("1/05/2013 0:00","1/06/2013 00:00") dose

Dear R-help, I was comparing SAS (I do not know what version it is) and R (version 2.6.0 (2007-10-03) on Linux) survival analyses with time-dependent covariates. The results differed significantly so I tried to understand on a short example where I went wrong. The following example shows that even when argument 'method' in R function coxph and argument 'ties' in SAS procedure

Let me expand a bit on Thomas's answer. Looking more closely at your data set you have the following: death time group 0 group 1 1.5 0/4 13/13 3 0/4 5/5 8 4/4 0 At time 1.5 group 1 had 13 deaths out of 13 at risk, group 0 had none. Time 8 doesn't have any impact on the fit, since only one group

Dear all, Is there a way to calculate the non-cumulative hazard (instantaneous hazard), which is the product of baseline hazard and exp{beta*covariate} ? I knew in survfit, we can get the estimator of cumulative baseline hazard, but how can we get the non-cumulative one? Thank you very much! Koshihaku -- View this message in context:

Dear all, Let's assume I have a clinical trial with two treatments and a time to event outcome. I am trying to fit a Cox model with a time dependent treatment effect and then plot the predicted survival curve for one treatment (or both). library(survival) test <- list(time=runif(100,0,10),event=sample(0:1,100,replace=T),trmt=sample(0:1,100,replace=T)) model1 <- coxph(Surv(time,

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

It appears that the degrees of freedom reported by logLik changed between R 1.4.1 and R 1.5.1. Is this true? Detail: > I have been using the lm and logLik functions in R to develop code using > version 1.4.1. When I run it on version 1.5.1, I'm getting different > degrees of freedom with the logLik function. Version 1.5.1 seems to give > one extra degree of freedom than

I'm using R on a linux redhat 8.0 installation. The special danish characters (ÅØÆæøå) are showed wrong on the screen. Example: yy<-matrix(c(0,2,1,1,8),ncol=1,dimnames=list(c("Brøæå","AGF","AB","Farum","FC-Kbh." ),c("Stemmer"))) barplot(yy[1:5],ylab=names(as.data.frame(yy)),main="5. grade.... ") Any hints? Cand.

I should hope that there is trouble, since "type III" is an undefined concept for a Cox model. Since SAS Inc fostered the cult of type III they have recently added it as an option for phreg, but I am not able to find any hints in the phreg documentation of what exactly they are doing when you invoke it. If you can unearth this information, then I will be happy to tell you whether

> Date: Fri, 7 Apr 2000 09:55:08 -0400 (EDT) > From: Faheem Mitha <faheem at email.unc.edu> [I have given a more meaningful subject line.] > On Fri, 7 Apr 2000, Prof Brian D Ripley wrote: > > > On Thu, 6 Apr 2000, Faheem Mitha wrote: > > > > > I'm running splus 5 on a solaris platform remotely, and running R on linux > > > on my home machine.

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Hello Everyone,   I'm learning how to perform various statistical analyses in R. I'm checking my understanding by replicating examples from my SAS books. Below is an attempt to replicate a Cox Proportional Hazards model with a time-varying covariate. I think I'm doing this correctly but am not completely sure. I would appreciate it if someone could double-check my results. In case

I am trying to find methods for testing and visualizing calibration to Cox models with time-depended coefficients. I have read this nice article <http://journals.sagepub.com/doi/10.1177/0962280213497434>. In this paper, we can fit three models: fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) p <- log(predict(fit0, newdata = data1, type = "expected")) lp

Hi there, I perform a stratified cox and then I need the strata as a  numeric array "straft.ln" ft.ln <- coxph(Surv(times,deaths)~ages+chemos+chemos:f1+chemos:f2+horms+horms:f1+horms:f2+grades+grades:f1+grades:f2+positives+positives:f1+positives:f2+sizes+sizes:f1+sizes:f2+strata(stra),data=ddd) basehazzft.ln=basehaz(ft.ln,centered=FALSE) H0ft.ln=c(basehazzft.ln[,1])

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

Dear R-users, I have two samples with individuals that are in more than one of the samples and individuals that are only in one sample. I have been trying to do a pooled hazard model, stacking one sample below the other, with aftreg and time-dependent covariates. The idea behind is to see aggregate effects of covariates, but need to control for ther effects of same individuals in both samples

Dear R-help Community, I'm currently struggling with some issues extending the proportional Cox model with time-dependent coefficients and could really need some help. Since I'm not experienced in adding code in an email in a nice way I add the link to my question and code: https://stats.stackexchange.com/questions/297052/time-dependend-coefficients-in-a-cox-model Essentially, I try

DeaR users. <framework> These days i'm working on fitting an extended Cox model with time-dependent covariables and possibly time-varying effects. My data are in counting process format as described in Therneau&Grambsh's `Modeling Survival Data', page 68. I'm trying to follow Harrell's `Regression Modeling Strategies' advices for the choice of my model. This

> ... interactions between covariables and time. A model such as "coxph(Surv(ptime, pstat) ~ age + age*ptime, ...." is invalid -- it is not at all what you think. If cph flags this as an error that is a good thing: I should probably add the same message to coxph. > Is is somewhat sensible to use cox.zph() to investigate which variables need time interaction... The cox.zph

I am having trouble formatting some survival data to use in a time dependent cox model. My time dep. variable is habitat and I have it recorded for every day (with some NAs). I think it is working properly except for calculating the death.time. This column should be 1s or 0s and as I have it only produces 0s. Any help will be greatly appreciated.