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RE: st: multiple state surv. analysis
I made a silly confusion yesterday about "stratification" in this
context. Steve MacKay's message reminded me of this. As he said, in
effect, pooling spells for all types of transitions, and then allowing
stratification by type of transition, allows for different baseline
hazards but imposes equality of each regressor coefficient across the
different processors. This is an even stronger (and inappropriate)
assumption than I referred to in my original message. At the very least,
model spells for each transition type separately.
Professor Stephen P. Jenkins <email@example.com>
Institute for Social and Economic Research
University of Essex, Colchester CO4 3SQ, U.K.
Tel: +44 1206 873374. Fax: +44 1206 873151.
> -----Original Message-----
> From: firstname.lastname@example.org
> [mailto:email@example.com] On Behalf Of Jenkins S P
> Sent: 22 November 2003 17:44
> To: firstname.lastname@example.org
> Subject: Re: st: multiple state surv. analysis
> On Sat, 22 Nov 2003, Maryah Fram wrote:
> > Hi -- I'm a new stata user, and trying to do an analysis in
> stata for
> > which I have syntax in R, as follows:
> > coxph(Surv(DUR,UNC)~B+J+Q16+strata(STRT)+cluster(ID)).
> > I'm looking at transitions between unemployment and work
> status during
> > a two-year study, where DUR is time between transitions,
> UNC indicates
> > final status is censored, B, J and Q16 are covariates, ID is the
> > individual's id number, and STRT is a 4-level
> stratification variable
> > indicating the type of transition (job to job, no job to
> job, etc.).
> > I can't figure out how to handle the STRT in Stata. Could someone
> > point me to the correct place in the manual? Or give any advice?
> You have multi-state transition information for each person
> (ID). There are a number of transition types, and each person
> may contribute multiple spells relating to each type
> (complete or censored). IMHO it is misleading to refer to
> your STRT variable as a "stratification" variable (in the
> complex survey sense). Nor should you simply pool transitions
> of the different types (as you appear to do -- though I admit
> that I do not know R).
> Standard survival models -- those available in Stata (and all
> other packages that I know of) -- assume that there is one
> potential transition type ("single risk" models) and also
> that each person contributes a single spell ("single spell"
> analysis). If there were no unobserved characteristics
> affecting transitions, then it would be ok to pool spells for
> each person, and model each transition type separately -- but
> this assumption is typically implausible. (Ceteris paribus,
> persons prone to exit quickly from a spell of unemployment
> are also likely to be those who are slow to re-enter
> unemployment if they have a job, and so on.) Researchers have
> implemented this strategy, nonetheless, though often they
> have adjusted standard errors to take account of the repeated
> observations on the same individual (as if you clustered on
> ID in your case) -- see the clear discussion of analysis of
> multiple spells by e.g. Allison PA (1984) Event History
> Analysis, Sage.
> To estimate multi-state transition models in a more
> sophisticated manner takes you into advanced
> econometrics/stats (and advanced programming -- there are no
> easily accessible canned routines that I am aware of). The
> models are discussed in T Lancaster's 1990 monograph on the
> econometrics of transition data. You should see also the
> survey by Gerard van den Berg which is a chapter in a recent
> Handbook of Econometrics (Elsevier). (Gerard and his
> co-authors are probably the leading experts on these sorts of
> models, and have published many papers which discuss estimates of
> them.) You might also search using Google or similar on
> "mixed proportional hazard models".
> good luck
> Professor Stephen P. Jenkins <email@example.com>
> Institute for Social and Economic Research (ISER)
> University of Essex, Colchester CO4 3SQ, UK
> Phone: +44 1206 873374. Fax: +44 1206 873151.
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