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.
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') Is that correct? Am I going to get the expected-time-to-death that I would like to have? 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? Is this going to improve the prediction? (for my data, the time already passed should be an important variable). Any other suggestions or comments? Thank you! -- View this message in context: http://r.789695.n4.nabble.com/Survival-Analysis-and-Predict-time-to-death-tp4711198.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
