similar to: I wonder if cch function in Survival package can calculate time dependent covariate

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Dear all, I have a couple of questions regarding the survival:::cch function. 1) I notice that Prentice and Self-Prentice functions are giving identical standard errors (not by chance but by programming design) while their estimates are different. My guess is they are both using the standard error form from Self and Prentice (1986). I see that standard errors for both methods are

--------- begin included message --------- I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI

Case cohort function cch() is in survival package. In cch(), the prentice method is implemented like this: Prentice <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.00000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators ## Calculate Prentice estimate ent2 <- tenter ent2[cc==2] <-

Dear r-users, Does anyone know how to draw time-dependent survival curves? Example: Event outcome: CHD Time-dependent covariate: NSAID use, which changes over time for each subject I'm interested in survival curves stratified by NSAID use. I'd like to implement Simon & Makuch (1984) method. Is there a R package/function to draw this graph?

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

http://r.789695.n4.nabble.com/file/n4619765/survival_file.png Hi everyone, This is what my data looks like, I haven't included the covariates, the example would get too large. uid id date feverstart dumfever 130 75 346465 2011-04-11 <NA> 0 131 75 360287 2011-04-18 <NA> 0 132 75

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

Dear colleagues, following John Fox' advice in this article (http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf), I'm trying to create a new data frame to examine the differential survival curves from a combination of covariates. These are derived from a Cox Proportional Hazards model I fit to data about the diffusion of a particular policy across American

Jin Wang had an error. My original note specified a variable that was 1 for subjects NOT in the subcohort, so the correct coxph call is coxph(Surv(edrel, rel) ~ stage + histol + age + offset(-100*(subcohort==0)) + cluster(seqno), data =ccoh.data) This gives the same coefficients as the cch example, along with the infinitesimal jackknife or "robust" variance estimate.

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

Hi everyone, I am fitting a cox proportional hazard model with a continuous variable "x" as the covariate: fit<-coxph(Surv(time, status)~x) Now I wanted to make a plot of survival probability vs. the covariate, and the 95% confidence interval for the survival probability. It's just like a Kaplan-Meier Survival curve, except now the x axis represents the value of covariate, not

Using a p-value to make any kind of decision is questionable to begin with, and especially unreliable in choosing covariates in regression. Old studies, e.g. by Walls and Weeks and by Bendel and Afifi, have shown that if predictive ability is the criterion of interest and one wishes to use p-values for deciding whether to include a covariate, one should set the p-value bar very large, at 0.25 and

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

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

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, 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, 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 am using the survfit() function to produce Kaplan-Meier survival curves for several different groups. survfit (Surv() ~ cohort, data=d) Everything works fine, but I'd like to do something different. I have a 12 month survival curve (base) and I also have two survival curves based on some experiment that only go out to 3 months. What I would like to do is take the 3 month realized