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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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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