Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion.
There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something important) or too many (including unimportant things). 2. Get survival curves curves <- survfit(fit, newdata= _____) The newdata needs to include all the covariates in your model. 3. Summarize Note that you don't get a single number prediction for each subject, you get a set of survival curves. plot(curves[1]) for instance shows you the first one, plot(curves[2]) the second. print(curves) will give a 1 line per curve summary including the median, and optionally one of several versions of the mean. See the discussion in help(print.survfit). The mean is rarely used as a summary due to the fact that we don't see the whole distribution. (Use temp<- summary(curves); temp$table to use the printout values in further calculations.) ------------------- The same process applies for parametric survival using survreg. In return for specifying a distributional form, the predicted survival curve for a particular subject is completely defined. This includes the mean and all quantiles. Reliablity analysis (survival analysis in industry) uses parametric almost exclusively, since the tail of the distribution is of greatest interest. Your use of predict(,type='response') is almost correct, there is just the math detail that the Weibull fits on a log scale, so the returned value is a geometric mean time to death rather than an arithmetic mean. The suggestion to use ordinary regression on the observed times is wrong. Censored data is more complex than that. Terry Therneau ______________________________________________ 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.
