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). 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. ______________________________________________ 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.
