I would like to determine the probability of an event at a specific timepoint given the linear predictor of a given Cox model. For instance, assume that I fit the following model:
data(pbc) fit <- coxph(Surv(time, status)~ age, data=pbc) To extract the value of the linear predictor for each patient in the dataset: prd <- predict(fit, newdata=pbc, type="lp") However, what I am really interested in is the predicted probability from the Cox model that an individual will experience an event by some time t (say 2000 days in this example). To my knowledge, calculating this probability requires knowing one of the 3 baseline functions-the survivor function or the hazard function or the cumulative hazard function-estimated from the Cox model. I would like to know : 1) How do I extract the correct baseline hazard so that I can predict the individual probablility of failure at 2000 days, given a particular patient covariate pattern (in this case, a particular patient's age? Is the baseline hazard for the average patient covariates the ideal one? 2) Is there a better way of directly extracting these probabilities? Brant Inman [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch 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.