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From |
"Barksdale, Crystal" <cbarksda@jhsph.edu> |

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

Subject |
RE: st: Question about multinomial logistic regression and random effects with multiply imputed data |

Date |
Thu, 31 Jul 2008 09:50:51 -0400 |

Thank you Maarten, for your assistance and suggestions. Unfortunately, our group has already explored using the Stata program gllamm with our multiply imputed data, and it does not work with the "mim" commands. We are considering running the analyses on each multiply imputed dataset (separately), and then trying to combine the results manually; however, we are having some difficulty figuring out how to store the parameter estimates and standard errors for each variable and it's corresponding category. For example, if I run a multinomial logistic regression with an outcome variable with 3 categories, I will get three separate estimates of the predictor. It is not immediately clear how to save these three separate estimates and standard errors, to use ultimately in combining the estimates across the multiply imputed datasets. Any additional thoughts or suggestions are welcome. Thank you, Crystal Crystal L. Barksdale, Ph.D. Postdoctoral Fellow Johns Hopkins Bloomberg School of Public Health 624 N. Broadway Hampton House, 808 Baltimore, MD 21205 (410) 502-9344 (office) (410) 955-9088 (fax) -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: Tuesday, July 29, 2008 6:04 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Question about multinomial logistic regression and random effects with multiply imputed data --- Crystal Barksdale <cbarksda@jhsph.edu> wrote: > I am wondering how (and if) I can do a multinomial random effects > logistic regression with multiply imputed data. Multinomial random effects logistic regression is discussed in (Haan & Uhlendorf 2006). A nice point of Multiple Imputation is that it is not method-specific, all it requires is that the sampling distribution of the parameters is (approximately) Gaussian / normal. As long as this is (approximately) true, you can use Multiple Imputation for your multinomial random effects logistic regression. There are ofcourse other assumptions like the missing data needs to be MAR, and if you don't know what that means you'll have to start reading before you touch -ice- or -mim-. A good starting point is (Allison 2001). -- Maarten Allison, P. (2001) Missing Data, Thousand Oaks: Sage. Haan, P. and Uhlendorf, A. (2006) Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood. The Stata Journal, 6(2): 229--245. http://www.stata-journal.com/article.html?article=st0104 ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- __________________________________________________________ Not happy with your email address?. Get the one you really want - millions of new email addresses available now at Yahoo! http://uk.docs.yahoo.com/ymail/new.html * * 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: Question about multinomial logistic regression and random effects with multiply imputed data***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: Question about multinomial logistic regression and random effects with multiply imputed data***From:*"Crystal Barksdale" <cbarksda@jhsph.edu>

**Re: st: Question about multinomial logistic regression and random effects with multiply imputed data***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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