1. survreg() does NOT fit a proportional hazards model, a mistake repeated multiple times in your post
2. The coxph function operates on the risk scale: large values of Xbeta = large death rates = bad The survreg operates on the time scale: large values of xbeta = longer liftetime = good. 3. predict(fit, type='risk') = exp(predict(fit, type='linear')) in a Cox model returns an estimate of the relative risk for each subject. That is, his/her predicted death rate as compared to the others in the sample. It has no units of "years" or "days" or anything else. The predicted survival TIME for a subject is something else entirely. predict(fit, type='response') in a survreg model does give predicted survvival times. If you really want to understand the interrelationships of these things more deeply I think you need some textbook time. Read the book by Kalbfleisch and Prentice for accelerated failure time models, or even better Escobar and Meeker which comes from the industrial reliability view. For predicted survival from a Cox model see Chapter 10 of Therneau and Grambsch. The answers to your specific questions would be a document rather than an email. 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.
