similar to: Estimating survival?

> On Jun 1, 2019, at 12:59 PM, Peter Langfelder <peter.langfelder at gmail.com> wrote: > > On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel > <r-devel at r-project.org> wrote: >> >> In the next version of the survival package I intend to make a non-upwardly compatable >> change to the survfit object. With over 600 dependent packages

In the next version of the survival package I intend to make a non-upwardly compatable change to the survfit object.? With over 600 dependent packages this is not something to take lightly, and I am currently undecided about the best way to go about it.? I'm looking for advice. The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in the survfit object

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

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

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 UseR, I do not know if this a problem with me, my data or cph/survest in package design. The example below works with a standard data set, but not with my data, but I cannot locate the problem. Note that I am using an older package of survival to avoid a problem with the newly renamed function in survival meeting Design. Dieter # First, check standard example to make sure library(Design)

Dear R-helpers, I am working with 'cph' models from 'rms' library. When I build simple survival models, based on 'Surv(time, event)', everything is fine and I can make nice plots using plot(Predict(f, time=3)). However, recently I tried to be more specific and used 'Surv(start, stop, event)' type model. Using this model 'plot(Predict(f))' works OK, but

Dear R users, My apologies for the simple question, as I'm starting to learn the concepts behind the Cox PH model. I was just experimenting with the survival and rms packages for this. I'm simply trying to obtain the expected survival time (as opposed to the probability of survival at a given time t). I can't seem to find an option from the "type" argument in the predict

Dear Prof Harrell and everyone, My PC: Window XP service pack 3 and service pack 2 R version 2.8.1 and 2.9 alpha For the last 3 days, after updating R, my two computers have been facing problems when running existing and runable R commands that involves with Design package I attempt to use 'survest', but I failed all the times with R (both 2.8.1 and 2.9 alpha) being shut down

Hello R helpers: *My first message didn't pass trough filter so here it's again* I would like to obtain probability of an event for one single patient as a function of time (from survfit.coxph) object, as I want to find what is the probability of an event say at 1 month and what is the probability of an event at 80 months and compare. So I tried the following but it fails miserably. I

I am trying to generate predictions from a weibull survival curve but it seems that the predictions assume that the shape(scale for survfit) parameter is one(Exponential but with a strange rate estimate?). Here is an examle of the problem, the smaller the shape is the worse the discrepancy. ### Set Parameters scale<-10 shape<-.85 ### Find Mean scale*gamma(1 + 1/shape) ### Simulate Data

hello: my data looks like: time1  time2   event  catagoria 2004    2006        1            C 2004    2005        0            C 2005    2010        1            E 2007    2009        1            C 2006    2007        0            E 2008    2010        0            C 2008    2010        1            E ... and the census interval is 1 year I have tried  this

I wonder if anyone know why survest (a function in Design package) and standard survfit.coxph (survival) returned different confidence intervals on survival probability estimation (say 5 year). I am trying to estimate the 5-year survival probability on a continuous predictor (e.g. Age in this case). Here is what I did based on an example in "help cph". The 95% confidence intervals

Dear Thomas, The head of my dataset > head(wsuv) parcel sp time censo treatment species 1 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 2 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 3 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 4 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1

Hey, I am just starting to learn R now and I typed in this simple survival program: library(survival) t <- c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107) c <- c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0) data <- Surv(t,c) km <- survfit(data) summary(km) Call: survfit(formula = data) but everytime I run it I get this error: Error in

On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel <r-devel at r-project.org> wrote: > > In the next version of the survival package I intend to make a non-upwardly compatable > change to the survfit object. With over 600 dependent packages this is not something to > take lightly, and I am currently undecided about the best way to go about it. I'm looking

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 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 <-

On 6/1/19 1:32 PM, Marc Schwartz wrote: > >> On Jun 1, 2019, at 12:59 PM, Peter Langfelder <peter.langfelder at gmail.com> wrote: >> >> On Sat, Jun 1, 2019 at 3:22 AM Therneau, Terry M., Ph.D. via R-devel >> <r-devel at r-project.org> wrote: >>> In the next version of the survival package I intend to make a non-upwardly compatable >>> change

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!