RE: Modeling of two time-to-event outcomes
Nick
Your approach is an important first step. However, there remains the possibility of co-dependence in the marginal distribution of the data once you have included a common fixed effect in your models.
I'm not sure that this can be specifically implemented in NONMEM for odd-type data. If it can then I'm keen to learn more.
Steve
--
Quoted reply history
> -----Original Message-----
> From: owner-nmusers
> nmusers
> Sent: Wednesday, 22 July 2009 8:08 a.m.
> To: nmusers
> Subject: Re: [NMusers] Modeling of two time-to-event outcomes
>
> Manisha,
>
> It might be helpful if you could be more specific about what you mean
> by
> correlated event times e.g. one could image that the time to event for
> hospitalization for a heart attack and the time to event for death
> might
> be correlated because they both depend on the the status of
> atherosclerotic heart disease.
>
> A parametric approach would be to specify the hazards for the two
> events
> and include a common covariate (e.g. serum cholesterol time course,
> chol(t)) in the hazard e.g.
>
> h(hosp)sehosp*exp(Bcholhosp*chol(t))
> h(death)sedeath*exp(Bcholdeath*chol(t))
>
> The common covariate, chol(t), would introduce some degree of
> correlation between the event times.
>
> Nick
>
>
> Manisha Lamba wrote:
> > Dear NMusers,
> >
> > If anyone in the user group aware of approaches on developing
> > semi-parametric or parametric models for (joint modeling of) two
> > time-to-event endpoints, which are highly correlated?
> > Any suggestions/references/codes(NONMEM, R etc.) would be very much
> > appreciated!
> >
> > Many thanks!
> > Manisha
> >
> >
>
> --
> Nick Holford, Professor Clinical Pharmacology
> Dept Pharmacology & Clinical Pharmacology
> University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
> Zealand
> n.holford
> mobile: +33 64 271-6369 (Apr 6-Jul 20 2009)
> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford