[R] AFT-model with time-varying covariates and left-truncation

[Philipp Rappold]

Thanks for your super-fast reply.

I realized you're totally right: My problem is not left truncation 
but missing data of time-varying covariates.

In my special case, two conditions are given by study design:
(1) A lot of subjects are missing all of their (time-varying) 
covariates for a certain period of time AND...
(2) ...if this is the case, this period always starts at lifetime=0.

You say missing time-varying covariates is a problem for both AFT 
and PH models. My question now is:
(a) Is there any solution to this problem (for either AFT or PH)?
(b) And if yes: It would be great if you could point me in the right 
direction (eg. literature, name of method,...).

Thanks a lot for you effort, I highly appreciate it!

All the best
Philipp


Göran Broström wrote:
> On Thu, Jan 28, 2010 at 2:32 PM, Philipp Rappold
> <philipp.rappold at 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 at 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.
>>
> 
> 
>