NONMEM Users Network Archive

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Re: Modeling of two time-to-event outcomes

From: A.J. Rossini <blindglobe>
Date: Wed, 22 Jul 2009 07:31:11 +0200

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
> Nick
>
> Your approach is an important first step.  However, there remains th=
e possibility of co-dependence in the marginal distribution of the data onc=
e 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
>> 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)=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 m=
uch
>> > 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
>
>



--
best,
-tony

blindglobe
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 - 01:31:11 EDT

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