On Thu, Jan 28, 2010 at 2:32 PM, Philipp Rappold <philipp.rapp...@gmail.com> wrote: > Dear Prof. Broström, > Dear R-mailinglist, > > first of all thanks a lot for your great effort to incorporate time-varying > covariates into aftreg. It works like a charm so far and I'll update you > with detailled benchmarks as soon as I have them. > > I have one more questions regarding Accelerated Failure Time models (with > aftreg): > > You mention that left truncation in combination with time-varying covariates > only works if "...it can be assumed that the covariate values during the > first non-observable interval are the same as at the beginning of the first > interval under observation.". My question is: Is there a way to use an AFT > model where one has no explicit assumption about what values the covariates > have before the subject enters the study (see example below if unclear)? For > me personally it would already be a great help to know if this is > statistically feasible in general, however I'm also interested if it can me > modelled with aftreg.
The AFT model with time-fixed acceleration factor a is S(t; a) = S_0(at) for some S_0. With a time-varying a = a(t), this becomes S(t; a) = S_0(\int_0^t a(s) ds), and in order to evaluate that you need the full history of a at each t > 0. > EXAMPLE (to make sure we're talking about the same thing): > Suppose I want to model the lifetime of two wearparts A and B with > "temperature" as a covariate. For some reason, I can only observe the > temperature at three distinct times t1, t2, t3 where they each have a > certain "age" (5 hours, 6 hours, 7 hours respectively). Of course, I have a > different temperature for each part at each observation t1, t2, t3. > Unfortunately at t1 both parts have not been used for the first time and > already have a certain age (5 hours) and I cannot observe what the > temperature was before (at ages 1hr, 2hr, ...). The important thing here is whether you have left-truncated _lifetimes_ or not. Your example is about missing observation(s) on a covariate, which is a different problem. But a problem. And not only for the AFT model, but for the PH model as well. Göran > Thanks a lot for your help! > > All the best > Philipp > > ______________________________________________ > 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. > -- Göran Broström ______________________________________________ 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.