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Re: st: correlated Competing Risks
"Chiara Mussida" <email@example.com>
Re: st: correlated Competing Risks
Thu, 17 Apr 2008 14:10:18 +0200
Thanks a lot Prof. Jenkins,
I'm going to explore the literature you kindly suggested below,
and I will let you know about the evolution of these estimates.
by tha way, I found really useful the material of your egards2005
summer school at Essex,
congratulation. It is really difficult to find valuable material on
these issues (duration & CR), and I hope to sort these out in a proper
way. Is there any other planned course there in essex? I would really
be pleased to have this info, since a visiting period on these topics
is what I really need to go on with my phD thesis.
On 17/04/2008, Stephen P. Jenkins <firstname.lastname@example.org> wrote:
> Date: Wed, 16 Apr 2008 12:21:05 +0200
> From: "Chiara Mussida" <email@example.com>
> Subject: st: correlated Competing Risks
> Hi guys,
> is there a specific command to estimate Seemeengly Unrelated Piecewise
> constant hazard model?
> more precisely, I have a CR framework that already gave me almost
> satisfactory results with piecewise constant hazard rate models
> estmations. Anyway, this modelisation is employed with the underlying
> assumption of independence of CR. If I want to verify the existence of
> correlation between my residuals (coming from different model
> estimates), how can I proceed in terms of stata command?
> thanks a lot
> There is no canned Stata module in the public domain to estimate these
> models that I am aware of. To /test/ whether there are correlated
> random effects, there are probably Lagrange Multiplier tests available
> that do not require estimation of a full joint correlated hazard
> model. Do a literature search. Alternatively, to use a likelihood
> ratio test, you would need to fit the correlated CR hazard model as
> well as the ICR one, i.e. model the hazards jointly. This is a
> non-trivial task -- see chapter 8 of my Survival Analysis manuscript
> at the URL below. See also the encyclopaedic survey paper by G van
> der Berg on "Duration Models", ch 55, in Handboook of Econometrics Vol
> 5, JJ Heckman & E Leamer (eds), 2001, Elsevier.
> The answer to your question may also depend on whether you are
> treating survival times as continuous or interval-censored (a.k.a.
> "grouped" or "discrete"). "Piecewise constant" refers to the shape of
> the baseline hazard typically, and can occur in both continuous and
> interval-censored models.
> It is not immediately obvious to me that application of -suest-, as
> suggested by Maarten Buis, is appropriate for such tests because of
> the nature of the likelihoods involved. There's one case where it
> /might/ be, though. It can be shown that, for interval censored data,
> the likelihood for a CR model can be approximated by a multinomial
> logit type likelihood. (And if intervals are narrow / interval hazards
> 'small', the approximation is usually quite good.) See Chapter 8 op.
> cit. In this particular case, an independent CR model can be fitted
> using -mlogit-, and a test for correlated unobservables is similar to
> the standard IIA test for MNL models. And the latter is something
> that is discussed under [R] -suest-. Note too the discussion of a MNL
> model with correlated random intercepts by Haan & Uhlendorff in Stata
> Journal 2006, 6(2). Whether the MNL-based approach is suitable is
> something you have to assess yourself.
> Professor Stephen P. Jenkins <firstname.lastname@example.org>
> Director, Institute for Social and Economic Research
> University of Essex, Colchester CO4 3SQ, U.K.
> Tel: +44 1206 873374. Fax: +44 1206 873151.
> Survival Analysis using Stata:
> Downloadable papers and software: http://ideas.repec.org/e/pje7.html
> Learn about the UK's new household panel survey, the United Kingdom
> Household Longitudinal Study: http://www.iser.essex.ac.uk/ukhls/
> Contribute to the consultation on content:
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> * http://www.ats.ucla.edu/stat/stata/
Doctoral school of Economic Policy
Catholic University, piacenza (Italy)
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