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Re: st: Predicted probabilities in a competing risks model in discrete time?


From   LachZak@web.de
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Predicted probabilities in a competing risks model in discrete time?
Date   Wed, 06 Aug 2008 19:59:35 +0200

Hi Steve,
I meant the following two papers:
Jenkins, S.P. (1995): Easy ways to estimate discrete time duration models, Oxford Bulletin of Economics and Statistics, 57, 129-138.
Allison, P. (1982): Discrete time methods for the analysis of event histories, pp. 61-98, in Sociological Methodology (ed. by S. Leinhardt).
These models were developed for intrinsically discrete time data, assuming a particular functional form for the destination-specific hazards in the competing risks framework., namely, hazard to destination A = exp(betaA*X)/[1+exp(betaA*X)+exp(betaB*X)] The resulting likelihood function is exactly the same as for a "standard" multinomial logit.
In Stata, estimation works as follows: Using expand, you create a dataset in person-month format and estimate it using a command as the following:
mlogit depvar regressors f(time)
My question is: Does it make any sense to interpret predicted probabilities after this estimation command, e.g. something like
prvalue, x(female=1) rest(mean) ?
Sorry for the first post, but this was my first try with Statalist...
Best, Katharina
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