Dear R users, I know, this is the second time i return on this topic. Sorry, but this analysis is of great value for me, and i hope someone can help me.
I need to model a time-varying effect in a Cox model. Briefly explained here: http://books.google.com/books?id=9kY4XRuUMUsC&lpg=PP1&hl=it&pg=PA147#v=onepage&q&f=false http://finzi.psych.upenn.edu/R/Rhelp02/archive/52828.html It seems that SAS is more well-suited to this purpose, as explained in the links above, but i do not have enough time to learn SAS. I think i should use something like this: cph(Surv(start,end,status)~a.factor+a.factor:start) but i'm not sure, specially after Thernau's reply to my previous post. Assuming i'm doing well, i'd prefer to use cph because of the rms bootstrap validation facilities. Unfortunately cph doesn't wanto to model an interaction term without the both corresponding main effects: > fit.cph <- cph(TACE.Surv~PS+PS:TIME.start,data=DatasetTACE.imputati) Error in if (!length(fname) || !any(fname == zname)) { : missing value where TRUE/FALSE needed coxph runs without complaining. I could make the design matrix columns by hand, but this is generally not recommended, isn't it? Any sort of comment will be appreciated. Thank you very much. Marco Barbàra. ______________________________________________ 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.
