similar to: Time-dependent covariates in coxph model

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,

This message was also sent to the MEDSTATS mailing list, so here is the reply I posted to that: Philippe, The machinery to use is to split follow-up time so finely that you can safely assume that rates are constant in each interval, and then just stuff it all into a Poisson model. This allows you to use any kind of time-dependent variables as well as accommodating competing risks. In the Epi

Dear R users: Analysis of the impact of a time-dependent covariate (GVHD or use of steroid after bone marrow transplantation) on two competing endpoints (invasive fungal infection and death) is frequently encountered in the setting of BMT data. Coxph package can be used as the following: for the analysis of GVHD: > gvhd -> coxph(Surv(start,stop,status = =1) ~ GVHD, data=bmt.data)

Hi I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another

Hi there. I have the following graph: http://www.nabble.com/file/p10928148/map.jpg However, some datapoints occur at the same place as other datapoints and are so layered on top of each other. I would like to know if there is any possible way in which I could view those datapoints that are layered on top of each other ...maybe by rotating using latitude to show the datapoints underneath (but

Hello I am using glmmBUGS to fit a multilevel model. Treatments are nested in Course are nested in Patients. The predicted variable in total EEG duration. The predictors are: at the observation level : Medication dose at the Course level: Weight in KG and Age at the Patient level: Weight in KG and Age I am trying to fit a multilevel model as in lmer, but in BUGS. Here is an example of the

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Thanks, Martin. Based on my previous post, I thought of a more general formulation of my question that I think would be helpful to ask here. What's the best way to build an R object that links multiple datapoints about different people? I mean, I happen to have datasets that have individual gene expression data tied to individual patient characteristics (how long they survived, age, gender,

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 R friends, this is an off-topic question. Could you please point me in ways (e.g., references or even R code) for simulating time varying covariates in a survival analysis setting. Thanks in advance for any responses. yours sincerely, Jin

Dear R users, is there a way, I mean a package, to perform a competing risk model which can handle time dependent covariates ? my main covariate (additional treatment to patients) appears not to follow the proportional hazards assumption, its effect being observed after one year of treatment but not before (this is expected / makes sense on a clinical point of view). SO I was planning to use a

Dear R-community, Dear Prof. Therneau, I would like to fit an AFT-model with time-dependent covariates and right-censored data. Searching the mailing list for information on the subject, I found some old posts which said it didn't work back then. My questions: (1) Has this kind of fitting already been implemented in the survival library in R? (2) If not: Are there any alternatives/

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:

Hi, I managed to make R core dump (linux and Mac OSX versions), but I think I've figured out why. First, here's the message I get on core dump (on linux - no message on Mac): R: ../inst/include/tnt/fmat.h:529: TNT::Vector<T> TNT::matmult(const TNT::Fortran_Matrix<T>&, const TNT::Vector<T>&) [with T = double]: Assertion `A.num_cols() == x.dim()'

Answering on a mail from >From Keith Wong <keithw_at_med.usyd.edu.au> Date Sun 04 Jul 2004 - 17:21:36 EST Subject [R] Random intercept model with time-dependent covariates, results different from SAS Hi all I've got a question about the degrees of freedom in a mixed model, calculated with lme from the lme4 package. Since I've no access to the original data

Dear all, I'm asking this question again as I didn't get a reply last time: I'm doing a survival analysis with time-dependent covariates. Until now, I have used a simple Cox model for this, specifically the coxph function from the survival library. Now, I would like to try out an accelerated failure time model with a parametric specification as implemented for example in the survreg

I have a dataset with event=death time (from medical examination until death/censoring) dose (given at examination time) Two groups are considered, a non-exposed group (dose=0), an exposed group (dose between 5 and 60). For some reason there is a theory of the dose increasing its effect over time (however it was only given (and measured) once = at the time of examination). I tested a model:

Hi all, I am looking at the tutorial/appendix from John Fox on ?Cox Proportional-Hazards Regression for Survival Data? available here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf I am particularly interested in modelling survival with time-dependent covariates (Section 4). The data look like this: > Rossi.2[1:50,] start stop arrest.time week arrest fin