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From |
"Justin B Echouffo Tcheugui" <jbe24@medschl.cam.ac.uk> |

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

Subject |
RE: st: Multilevel modelling of survival data |

Date |
Tue, 17 Mar 2009 10:24:31 -0000 |

Thanks for this, very clear Justin B. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: 16 March 2009 20:39 To: statalist@hsphsun2.harvard.edu Subject: RE: st: Multilevel modelling of survival data --- On Mon, 16/3/09, Justin B Echouffo Tcheugui wrote: > I have smoking as one of my outcome in a cluster > randomised trial. The unit of randomisation are clinics. > I want to explore the difference between groups in term > of smoking at follow-up > > 1- I first used logistic > regression using the following commands to derive the > odds ratio -gllamm- and -xtmelogit- > > Is there any advantage of using one or the other command? -xtmelogit- is quicker, and since you are only estimating a random intercept model you could also use -xtlogit-, which is even quicker. > 2- Later on, I was advised to analyse smoking as a > continuous variable in a linear fashion expressing my > result as the adjusted difference in proportion of smokers > between the groups. > > Is there reason why one should prefer -xtmixed- instead of > xtmelogit for a binary variable? I am no fan of using linear models for categorical data, and that is an understatement. The arguments that I have heard in their favor come roughly in two flovours: 1) "I (or my readers) don't know how to interpret odds ratios" The answer is: it is not that hard, the odds is how many successes for every failure, and is a measure of the likelihood of success. The odds ratio is how many times larger this odds of success is for group 2 relative to group 1. 2) "The results of non-linear models like -logit- are a biased biased estimate of the causal effect, even when analyzing data obtained in a randomized experiment" The answer is: true, but the same is also true for the linear model when applied to a categorical dependent variable. So, I just would not bother with -xtmixed-, and stick with -xtlogit-. > Do I need absolutely need to correct the confidence > intervals given by the linear model? Probably, the residual error is not going to be homoskedastic. > If yes, is the following command the right one to use? I have no idea, primarily because I would not use the linear model in this case anyhow. -- Maarten ----------------------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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**:**RE: st: Multilevel modelling of survival data***From:*"Justin B Echouffo Tcheugui" <jbe24@medschl.cam.ac.uk>

**RE: st: Multilevel modelling of survival data***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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