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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 17 Apr 2012 13:18:09 +0100 |

Sure, but that is not my point here. Katya said her data were expanded by length of time. Suppose I am an observation, you are an observation, and so on, and you -expand- by (e.g.) years on Statalist, months on Statalist, days on Statalist. (a) The answer is different in terms of implied sample size and (b) you replace individual observations by blocks of otherwise identical observations. As I said, sounds dubious to me. If Katya explains that she didn't do that, fine. If Katya explains that it does make sense, fine. Nick On Tue, Apr 17, 2012 at 1:09 PM, Maarten Buis <maartenlbuis@gmail.com> wrote: > 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/

**Follow-Ups**:**Re: st: dropping vars from analysis under conditions***From:*Steve Samuels <sjsamuels@gmail.com>

**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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