similar to: Modelling survival with time-dependent covariates

I'm a new to the R world and I have been following the John Fox Appendix on Cox regression (http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf) to run some analyses. I have been able to generate models and some simple figures e.g.: >library(splines) #required for survival package >library(survival) #req for Coxph

Dear Mr. Therneau, Mr. Fox, or to whoever, who has some time... I don't find a solution to use the "survfit" function (package: survival) for a defined pattern of covariates with a Cox-Model including a time dependent interaction term. Somehow the definition of my "newdata" argument seems to be erroneous. I already googled the problem, found many persons having the

Hello All, I am trying to use the unfold function in RcmdrPlugin.survival library, which converts the survival data with time varying covariates to the counting process notation. The problem is somehow, the event indicator created is not correct. Below is the data, I am trying to convert: CASE TRT FAILTIME FAILCENS SEX AGE IGG0 IGG28 IGG42 IGG84 IGG364 26003 A 11.2033

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations

Hi all, I was trying to test the assumption of proportional hazards assumption, I used the cox.zph function >cox.zph(coxfit6) Results are: rho chisq p x1 -0.0396 1.397 2.37e-01 x2 0.1107 9.715 1.83e-03 x3 -0.0885 7.743 5.39e-03 x4 0.0366 1.092 2.96e-01 x5 0.0242 0.455 5.00e-01 GLOBAL

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

Hi, I've been banging my head against the following problem for a while and thought the fine people on r-help might be able to help. I'm using the survival package. I'm studying the survival rate of a population with a preexisting linear-like event rate (there are theoretical reasons to believe it's linear, but of course it's subject to the usual sampling noise) Some of the

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, I use the Andersen plot to check for proportional hazards assumption for a factor (say x) in the Cox regression model and obtained a straight line that pass through the origin. However, the formal test done by the R-function cox.zph, which is based on the plot of Schonefeld residuals against time, indicates that proportional hazards assumption is violated. Further, a plot of the score

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 have built a survival cox-model, which includes a covariate * time interaction. (non-proportionality detected) > I am now wondering how could I most easily get survival predictions from my model. > > My model was specified: > coxph(formula = Surv(event_time_mod, event_indicator_mod) ~ Sex + > ageC + HHcat_alt + Main_Branch + Acute_seizure +

Hi all, I am trying to detect association between a covariate and a disease outcome using R. This covariate shows time-varying effect, I add a time-covariate interaction item to build Cox model as follows: COX <- coxph(as.formula("Surv(TIME,outcome)~eGFR_BASE+eGFR_BASE:TIME"),ori.data); coef exp(coef) e(coef) z p eGFR_BASE

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

> ... 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

Buenas tardes. Tengo un data frame con el que me descargo una serie de datos municipalizados. Después extraigo los que corresponden al municipio que me interesa: paro.municipal <- paro.municipal[paro.municipal$provincia == "28" & paro.municipal$municipio == «115», ]. Todo correcto. El problema viene cuando quiero elegir más de un municipio: paro.municipal <-

Dear all, I am using the function ?unfold? from the ?RcmdrPlugin.survival? to convert my time-varying covariates dataset from wide to long. I managed to have it working for my data. However, the problem I have is that the observations after an event, won?t be dropped from the dataset. For example, see the dataframe below: the event occurs at 1.2 (event.time=1), but the 1.3 to 1.6 will remain in

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Hello, I got error message when applying the plot function to the cox.zph object to create the Schoenfeld residual plots. > plot(zph.revasFit[1]) Error in plot.window(xlim, ylim, log, asp, ...) : need finite 'ylim' values In addition: Warning messages: 1: NaNs produced in: sqrt(x$var[i, i] * seval) 2: no non-missing arguments to min; returning Inf in: min(x) 3: no

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

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