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st: correlated Competing Risks
"Stephen P. Jenkins" <firstname.lastname@example.org>
st: correlated Competing Risks
Thu, 17 Apr 2008 11:16:31 +0100
Date: Wed, 16 Apr 2008 12:21:05 +0200
From: "Chiara Mussida" <email@example.com>
Subject: st: correlated Competing Risks
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
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/
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