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From |
"Luis Ortiz" <luis.ortiz@upf.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: Accounting for sample selection in discrete-time survival models |

Date |
Thu, 17 Apr 2008 15:07:08 +0200 |

Dear Statalisters, I am carrying out survival analysis on the transition from a state A to another state B. But I suspect many of the variables possibly affecting this transition are also quite determinant in having people in my population of study (risk set). In other words, I suspect population in state A is not randomly selected, and many of my covariates may not be just affecting the transition from state A to state B, but also the mere inclusion of many individuals in state A. My even of study occurs in continuous time but observed survival times are grouped into intervals in my data. Given this restriction, I am using the command 'CLOGLOG', as suggested by Stephen Jenkins in his lessons. Does anyone know if 'cloglog' allows for any option that corrects for this problem of sample selection I have previously mentioned? Alternatively, is there any option that I could resort to? Moreover, I suspect that my main event of study naturally competes with another one. Both events are quite likely dependent on each other. Given the fact that I am forced to use discrete-time models, I am using a MULTINOMIAL LOGIT MODEL, as recently suggested by Stephen Jenkins to Pavlos C. Symeou's. Again, my question is similar to the previous one: do you know the possibility of carrying out a similar analysis but accounting for the fact that my sample is possibly not randomly selected? Many thanks for your attention. Luis Ortiz * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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