similar to: Novice question: Smooth interpolation of survival curve

Hello users, I would like to obtain a survival curve from a Cox model that is smooth and does not have zero differences due to no events for those particular days. I have: > sum((diff(surv))==0) [1] 18 So you can see 18 days where the survival curve did not drop due to no events. Is there a way to ask survfit to fit a nice spline for the survival?? Note: I tried survreg and it did not

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized

I am using plot(survfit(Surv(time,status) ~...) and would like to add error bars rather than the confidence intervals. Am I able to do this at specified times? e.g. when time = 20 & 40. leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) plot(leukemia.surv, lty = 2:3,xlim = c(0,50)) #can i add error bars at times 20 & 40? legend(100, .9, c("Maintenance", "No

Dear useRs, What is the syntax to obtain survival curves for single strata on many subjects? I have a model based on Surv(time,response) object, so there is a single row per subject and no start,stop and no switching of strata. The newdata has many subjects and each subject has a strata and the survival based on the subject risk and the subject strata is needed. If I do newpred <-

Hello, I'm using plot.survfit to plot cumulative incidence of an event. Essentially, my code boils down to: cox <-coxph(Surv(EVINF,STATUS) ~ strata(TREAT) + covariates, data=dat) surv <- survfit(cox) plot(surv,mark.time=F,fun="event") Follow-up time extends to 54 weeks, but the last event occurs at week 30, and no more people are censored in between. Is there a

Hello, I now receive an error message when obtaining a survival plot, which was not previously received with the same code. I recently updated all my packages. It does not seem to be a peculiarity of my data as I receive the error using data available in R. A plot is produced but I am uncertain regarding the error message. > library(surv2sample) > data(gastric) > fit <-

Hello, I'm trying to estimate a Cox proportional hazard model with time-varying covariates using coxph. The parameter estimates are fine but there is something wrong with the survival curves I get with survfit (results are not plausible). Let me explain why I think something's wrong. To make sure I'm setting up my data correctly to estimate a model with time-varying covariates, I

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, I fit a Cox PH model to estimate the cause-specific hazards (in a competing risks setting). Then , I compute the survival estimates for all the individuals in my data set using the `survfit' function. I am currently playing with a data set that has about 6000 observations and 12 covariates. I am finding that the survfit function is very slow. Here is a simple simulation example

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!

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

I have attempted to follow posting guidelines but I have failed to find out what I am doing wrong here. I am trying to use lines.survfit to plot a second curve onto a survival curve produced by plot.survfit. In my case this is to be a progression free survival curve superimposed upon an overall survival curve, but I will illustrate my problem using the example given in the help for

Hello, I am trying to compute the mdeian of the survival time from the function survfit: > fit <- survfit(Surv(time, status) ~ 1) > fit Call: survfit(formula = Surv(time, status) ~ 1) records n.max n.start events median 0.95LCL 0.95UCL 111 111 111 20 NA NA NA The results is NA? the fit$surv gives values between 1 and 0.749! Am I doing this correct?

Dear R-users I am trying to make an adjusted Kaplan-Meier curve (using the Survival package) but I am having difficulty with plotting it so that the plot only shows the curves for the adjusted results. My data come from a randomised controlled trial, and I would like the adjusted Kaplan-Meier curve to only show two curves for the adjusted survival: one for those on treatment (Treatment==1)

Did you not notice the conf.type = "none" argument to your survfit call and the associated documentation in the survfit help? -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sat, Aug 26, 2017 at 5:18 PM, Adrian Johnson

Hello all. I've been attempting to utilize the "survival" pkg ( http://cran.r-project.org/web/packages/survival/index.html), while reading through this guide (http://www.ms.uky.edu/~mai/Rsurv.pdf). I figured working through the guide would be the best way to go, before attempting my own data. I tried to utilize the Kaplain-Meier estimator as shown in the guide:

> Further I appreciate your new function survmean(). At the moment it > seems to be intended as internal, and not documented in the help. The computations done by print.survfit are now a part of the results returned by summary.survfit. See 'table' in the output list of ?summary.survfit. Both call an internal survmean() function to ensure that any future updates stay in

Hi, I'm sure this should be simple but I can't figure it out! I want to get the median survival calculated by the survfit function and use the value rather than just be able to print it. Something like this: library(survival) data(lung) lung.byPS = survfit(Surv (time, status) ~ ph.ecog, data=lung) # lung.byPS Call: survfit(formula = Surv(time, status) ~ ph.ecog, data = lung) 1

Dear Bert, thank you for suggestion. I am aware of R-help function. I must apologize, my earlier question could lead to assumptions otherwise. As you can see below, I only get Std. error but not lower and Upper CIs. I was wondering if there is another argument or method, could give CIs which I cannot find anywhere. Thanks Adrian > Nsurv <-