Thanks very much for your answers, David and Mattia. I understand that the baseline hazard in a Cox model is unknown and that this makes the calculation of expected survival difficult. Does this change when I move to a survreg model instead?
I think I'm OK with estimating a Cox model (or a survreg model) as I've done so in the past. But I'm lost with the different options in the prediction part (e.g., linear, quantile, risk, expected, ...). Is there any document that can provide an explanation what these options mean? Sorry in case these questions are naive ... hope they're not too stupd ;-) On Thu, Nov 11, 2010 at 5:03 PM, Mattia Prosperi <ahn...@gmail.com> wrote: > Indeed, from the predict() function of the coxph you cannot get > directly "time" predictions, but only linear and exponential risk > scores. This is because, in order to get the time, a baseline hazard > has to be computed and it is not straightforward since it is implicit > in the Cox model. > > 2010/11/11 David Winsemius <dwinsem...@comcast.net>: > > > > On Nov 11, 2010, at 3:44 AM, Michael Haenlein wrote: > > > >> Dear all, > >> > >> I'm struggling with predicting "expected time until death" for a coxph > and > >> survreg model. > >> > >> I have two datasets. Dataset 1 includes a certain number of people for > >> which > >> I know a vector of covariates (age, gender, etc.) and their event times > >> (i.e., I know whether they have died and when if death occurred prior to > >> the > >> end of the observation period). Dataset 2 includes another set of people > >> for > >> which I only have the covariate vector. I would like to use Dataset 1 to > >> calibrate either a coxph or survreg model and then use this model to > >> determine an "expected time until death" for the individuals in Dataset > 2. > >> For example, I would like to know when a person in Dataset 2 will die, > >> given > >> his/ her age and gender. > >> > >> I checked predict.coxph and predict.survreg as well as the document "A > >> Package for Survival Analysis in S" written by Terry M. Therneau but I > >> have > >> to admit that I'm a bit lost here. > > > > The first step would be creating a Surv-object, followed by running a > > regression that created a coxph-object, using dataset1 as input. So you > > should be looking at: > > > > ?Surv > > ?coxph > > > > There are worked examples in the help pages. You would then run predict() > on > > the coxph fit with "dataset2" as the newdata argument. The default output > is > > the linear predictor for the log-hazard relative to a mean survival > estimate > > but other sorts of estimates are possible. The survfit function provides > > survival curve suitable for plotting. > > > > (You may want to inquire at a local medical school to find statisticians > who > > have experience with this approach. This is ordinary biostatistics these > > days.) > > > > -- > > David. > > > >> > >> Could anyone give me some advice on how this could be done? > >> > >> Thanks very much in advance, > >> > >> Michael > >> > >> > >> > >> Michael Haenlein > >> Professor of Marketing > >> ESCP Europe > >> Paris, France > > > > David Winsemius, MD > > West Hartford, CT > > > > ______________________________________________ > > R-help@r-project.org mailing list > > 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. > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.
