similar to: plotting coxph results using survfit() function

hi, is there a way of calculating of measuring dependence between two categorical variables. i tried using the chi square test to test for independence but i got error saying that the lengths of the two vectors don't match. Suppose X and Y are two factors. X has 5 levels and Y has 7 levels. This is what i tried doing >temp<-chisq.test(x,y) but got error "the lengths of the two

using an example from r online help > state <- c("tas", "sa", "qld", "nsw", "nsw", "nt", "wa", "wa", "qld", "vic", "nsw", "vic", "qld", "qld", "sa", "tas", "sa", "nt", "wa", "vic",

Dear all, I'm having difficulty getting access to data generated by survfit and print.survfit when they are using with a Cox model (survfit.coxph). I would like to programmatically access the median survival time for each strata together with the 95% confidence interval. I can get it on screen, but can't get to it algorithmically. I found myself examining the source of print.survfit to

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

I am fitting a coxph model on a large dataset (approx 100,000 patients), and then trying to estimate the survival curves for several new patients based on the coxph object using survfit. When I run coxph I get the coxph object back fairly quickly however when I try to run survfit it does not come back. I am wondering if their is a more efficient way to get predicted survival curves from a coxph

Dear users, In a study with recurrent events: My objective is to get estimates of survival (obtained through a Cox model) by rank of recurrence and by treatment group. With the following code (corresponding to a model with a global effect of the treatment=rx), I get no error and manage to obtain what I want : data<-(bladder)

Hi Dear R-users I have a database with 18000 observations and 20 variables. I am running cox regression on five variables and trying to use survfit to plot the survival based on a specific variable without success. Lets say I have the following coxph: >library(survival) >fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian) >fit what I am trying to do is plot a survival

Dear All, I want to generate survival curve with cox model but I want to estimate the coefficients using glmnet. However, I also want to include a strata() term in the model. Could anyone please tell me how to have this strata() effect in the model in glmnet? I tried converting a formula with strata() to a design matrix and feeding to glmnet, but glmnet just treats the strata() term with one

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 I?ve encountered, using the lung dataset. The first problem is the warning: To me, it seems that there are too many dummies

Hello All, Does anyone know why length(fit1$time) < length(fit2$n) in survfit.coxph output? Why is the predicted time length is not the same as the number of samples (n)? I tried: example(survfit.coxph). Thanks, parmee > fit2$n [1] 241 > fit2$time [1] 0 31 32 60 61 152 153 174 273 277 362 365 499 517 518 547 [17] 566 638 700 760 791

Hi! I am having a few problems with coxph function, I had the same problem with the use of survfit. Here it is: when calling 'M22<-coxphw(Surv(V1,V2,V4)~V5, data=XTDV, id=XTDV$V3, * prentice*= ~V5, robust=TRUE, censcorr=TRUE)' knowning that my data is: > XTDV[1:10,1:3] V1 V2 V3 V4 V5 1 0 36.39706 36 1 343.2224 2 0 36.39706 943 0 161.5931 3 0 36.39706

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

I have a model with quant vars only and the error message does not make sense: (mod1 <- coxph(Surv(time=strt,time2=stp,event=(resp==1))~ +incpost+I(amt/1e5)+rate+strata(termfac), subset=dt<"2010-08-30", data=inc,method="efron")) Call: coxph(formula = Surv(time = strt, time2 = stp, event = (resp == 1)) ~ +incpost + I(amt/1e+05) + rate + strata(termfac),

What is the best way to report the standard error when publishing Kaplan-Meier plots? In my field (Vascular Surgery), practitioners loosely refer to the "10% error" cutoff as the point at which to stop drawing the KM curve. I am interpreting this as the *standard error of the cumulative hazard*, although I'm having a difficult time finding some guidelines about this (perhaps I am

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

Hello to the Samba devs and mailing list subscribers, I've run into a bit of trouble getting a new domain member server setup. I've got three Ubuntu 14.04 64 bit VMs running the latest stable build of Samba built from source acting as Domain Controllers. I've got a fourth physical machine running Ubuntu 16.04 64 bit running the canonical distribution samba (Version 4.3.9-Ubuntu)

Dear All, I have a question regarding the output of survfit() when I supply a Cox model. Lets say for example: library(survival) fit <- coxph(Surv(time, status == 2) ~ factor(spiders), data = pbc) fit # HR for spiders is significant newdata <- data.frame(spiders = factor(0:1)) sf <- survfit(fit, newdata = newdata) sum.sf <- summary(sfit, times = c(2000, 2500, 3000)) # survival

I am wondering how to estimate the survival curve for a particular case(s) given a coxph model using this example code: #fit a cox proportional hazards model and plot the #predicted survival curve fit <- coxph( Surv(futime,fustat)~resid.ds+strata(rx)+ecog.ps+age,data=ovarian[1:23,]) z <- survfit(fit,newdata=ovarian[24:26,],individual=F) zs <- z$surv zt <-

Hi all, I am trying to connect Teradata with R using Windows. Due I need to install any specific package or what? I am bit clue-less. Can someone help. Regards, Saj _________________________________________________________________ [[alternative HTML version deleted]]

Terry, I recently noticed the censor argument of survfit. For some analyses it greatly reduces the size of the resulting object, which is a nice feature. However, when combined with the id argument, only 1 prediction is made. Predictions can be made individually but I'd prefer to do them all at once if that change can be made. Chris ##################################### # CODE # create