Ooops. I meant to drop that other message but hit the send icon instead.
On Aug 17, 2015, at 3:39 PM, Bert Gunter wrote: > David: > > I may have misunderstood you here, specifically: > > "As such I would ask if you really wanted to use a parametric survival > model in the first place? " > > The K-M curve is , of course, a **non-parametric** fit, and that is > why there can be no mean survival time unless the last point is a > death. > > If you use the sample data to estimate a **parametric** model, then, > of course, you can estimate mean survival time (at any covariate > value) as the mean of the predicted parameter estimates (e.g. through > a link function). Agree. I should have thought about that. I can post a clarification since this also mean my earlier comments about getting mean and median were off-target. Best; David. > > I would certainly agree that the OP seems pretty confused about all > this. And apologies if I have misunderstood. > > Cheers, > Bert > > > Bert Gunter > > "Data is not information. Information is not knowledge. And knowledge > is certainly not wisdom." > -- Clifford Stoll > > > On Mon, Aug 17, 2015 at 1:51 PM, David Winsemius <dwinsem...@comcast.net> > wrote: >> >> On Aug 17, 2015, at 12:10 PM, survivalUser wrote: >> >>> Dear All, >>> >>> I would like to build a model, based on survival analysis on some data, that >>> is able to predict the /*expected time until death*/ for a new data >>> instance. >> >> Are you sure you want to use life expectancy as the outcome? In order to >> establish a mathematical expectation you need to have know the risk at all >> time in the future, which as pointed out in the print.survfit help page is >> undefined unless the last observation is a death. Very few datasets support >> such an estimate. If on the other hand you have sufficient events in the >> future, then you may be able to more readily justify an estimate of a median >> survival. >> >> The print.survfit function does give choices of a "restricted mean survival" >> or time-to-median-survival as estimate options. See that function's help >> page. >> >>> Data >>> For each individual in the population I have the, for each unit of time, the >>> status information and several continuous covariates for that particular >>> time. The data is right censored since at the end of the time interval >>> analyzed, instances could be still alive and die later. >>> >>> Model >>> I created the model using R and the survreg function: >>> >>> lfit <- survreg(Surv(time, status) ~ X) >>> >>> where: >>> - time is the time vector >>> - status is the status vector (0 alive, 1 death) >>> - X is a bind of multiple vectors of covariates >>> >>> Predict time to death >>> Given a new individual with some covariates values, I would like to predict >>> the estimated time to death. In other words, the number of time units for >>> which the individual will be still alive till his death. >>> >>> I think I can use this: >>> >>> ptime <- predict(lfit, newdata=data.frame(X=NEWDATA), type='response') >> >> I don't see type="response" as a documented option in the `?predict.survreg` >> help page. Were you suggesting that code on the basis of some tutorial? >> >>> Is that correct? Am I going to get the expected-time-to-death that I would >>> like to have? >> >> Most people would be using `survfit` to construct survival estimates. >> >>> >>> In theory, I could provide also the time information (the time when the >>> individual has those covariates values), should I simply add that in the >>> newdata: >>> >>> ptime <- predict(lfit, newdata=data.frame(time=TIME, X=NEWDATA), >>> type='response') >>> >>> Is that correct? >> >> This sounds like you are considering time-varying predictors. Adding them as >> a 'newdata' argument is most definitely not the correct method. As such I >> would ask if you really wanted to use a parametric survival model in the >> first place? The coxph function has facilities for time-varying covariates. >> >> >>> Is this going to improve the prediction? >> >> It would most likely severely complicate prediction. Survival estimates may >> be more problematic in that case on theoretical grounds. >> >>> (for my data, the >>> time already passed should be an important variable). >>> >>> Any other suggestions or comments? >>> >>> Thank you! >>> >> >> R-help at r-project.org >> >> The real Rhelp mailing list .... not the impostor Rhelp at Nabble >> >> -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> -- >> >> David Winsemius >> Alameda, CA, USA >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
