> > ... interactions between covariables and time. > > A model such as "coxph(Surv(ptime, pstat) ~ age + age*ptime, ...." > is invalid -- it is not at all what you think.
Actually what i'm trying to fit is coxph(Surv(start,end,event)~age+age:start) to model a time-varying effect \beta(t)=a+b*t. Conceptually, is not H_0:b=0 the hypothesis tested by cox.zph()? (i know: i have to study more deeply this topic, my copy of your book is on the way to my house) > > > Is is somewhat sensible to use cox.zph() to investigate which > variables need time interaction... > > The cox.zph function is primarily graphical; I would respond to your > question with "is it good to look at scatterplots before fitting a > linear model?" My answer to this is emphatically yes. > > Terry Therneau > > Thank you, 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.
