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From |
"K.O. Ivanova" <K.O.Ivanova@uvt.nl> |

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

Subject |
RE: st: dropping vars from analysis under conditions |

Date |
Tue, 17 Apr 2012 12:15:59 +0000 |

Dear Maarten and Nick, Thank you for your answers. I now realize that my question was far from clear and I think that I managed to resolve it in a much simpler way in the meantime. Just to clarify a few things - yes, I am running an identical analysis for 5 separate countries but am treating the results as descriptive. And yes, I am estimating a discrete time survival model. Once again, thank you for the suggestions! Katya -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten Buis Sent: dinsdag 17 april 2012 14:09 To: statalist@hsphsun2.harvard.edu Subject: Re: st: dropping vars from analysis under conditions On Tue, Apr 17, 2012 at 12:35 PM, Nick Cox wrote: > Expansion by time spent also sounds very dubious. If that means # > observations for # units of time spent, well, the frequency > interpretation depends on units of time being discrete, and on which > units you use, and there is now a cluster structure. There are situations where this can make sense. This can be used as a trick to estimate a discrete time survival analysis model or a sequential logit model. In those cases the total contribution of each individual to the log-likelihood is the sum of the log-likelihoods of passing each step/period/transition. It does not matter if we first sum the contributions of each transition within a person and than sum over the person (which is what a purpose written program (might) do), or do the entire sum in one go (which is what you do when you expand). So, the expansion can be used as a computational trick with which you can estimate a survival model using programs that are not designed to estimate a survival model. Having said all that, using such tricks correctly is tricky. These programs are not designed for that kind of analysis, and there can easily be many options and post-estimation commands that will give you output that does not make sense in this case. One example I can think of right now is anything that relies on the sample size: e.g. BIC and AIC values, but there may be (many) more. It is now up to the user to understand what does and does not make sense. On the other hand Stata has a whole suit of programs specifically designed for analyzing survival data, see -help st-. Using these commands seem to me the safer option. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/ * * 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/

**References**:**st: dropping vars from analysis under conditions***From:*"K.O. Ivanova" <K.O.Ivanova@uvt.nl>

**Re: st: dropping vars from analysis under conditions***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: dropping vars from analysis under conditions***From:*Maarten Buis <maartenlbuis@gmail.com>

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