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RE: Modeling of two time-to-event outcomes

From: Stephen Duffull <stephen.duffull>
Date: Wed, 22 Jul 2009 18:02:47 +1200

Anthony

We've been working with extreme value Copula functions for conjoining survival analyses in MATLAB. I wasn't sure, however, whether these could be implemented easily in NONMEM.

Steve

> -----Original Message-----
> From: A.J. Rossini [mailto:blindglobe
> Sent: Wednesday, 22 July 2009 5:31 p.m.
> To: Stephen Duffull
> Cc: Nick Holford; nmusers
> Subject: Re: [NMusers] Modeling of two time-to-event outcomes
>
> For 2 event-time responses, without regression, copula models are the
> common way of handling bivariate event time models. There are some
> extensions for regression approaches with them, but I havn't been
> following that literature.
>
> Another approach would be the Weissfield-Wei-Lin (not sure I got the
> first name correct) extensions to the cox model, but that is more like
> the GEE/Population average approach, which handles and accomodates the
> correlation structure indirectly rather than being specific about it
> as in the mixed-effects literature.
>
>
> The above are implemented in R, along with many variations. Check
> CRAN.
>
>
> On Wed, Jul 22, 2009 at 3:36 AM, Stephen
> Duffull<stephen.duffull ac.nz> wrote:
> > 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
> > --
> >
> >> -----Original Message-----
> >> From: owner-nmusers@globomaxnm.com [mailto:owner-
> >> nmusers lobomaxnm.com] On Behalf Of Nick Holford
> >> 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)=basehosp*exp(Bcholhosp*chol(t))
> >> h(death)=basedeath*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 land.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090
> >> mobile: +33 64 271-6369 (Apr 6-Jul 20 2009)
> >> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
> >
> >
>
>
>
> --
> best,
> -tony
>
> blindglobe il.com
> Muttenz, Switzerland.
> "Commit early,commit often, and commit in a repository from which we
> can easily roll-back your mistakes" (AJR, 4Jan05).
>
> Drink Coffee: Do stupid things faster with more energy!
Received on Wed Jul 22 2009 - 02:02:47 EDT

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