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From |
Joseph Coveney <[email protected]> |

To |
Statalist <[email protected]> |

Subject |
Re: st: matching Agresti's SAS results with Stata |

Date |
Fri, 03 Oct 2003 15:15:39 +0900 |

Bill Rising posted earlier this week: ---------------------------------------------------------------------------- Hello folks, Has anyone out there tried mimicking the ordinal logistic regression results from the insomnia example given the second edition of Agresti's book on Categorical analysis? I was trying to learn to use the gllamm package for Stata using Agresti, because his datasets are all on the web http://www.stat.ufl.edu/~aa/cda/sas/sas.html (and because I have the book). As one might guess from the URL, SAS is the package he used to get the results in his book. I'm stuck trying to mimic table 12.7 on page 514, where he compares estimates for a model using ordinal logistic regression (aka cumulative logit) for some insomnia data (found in table 11.4 on p.462). When I use Stata's ologit command to try to match his ML estimates, I instead match his marginal GEE estimates. If I try to mimic his use of ML random intercept models, using gllamm using 'binomial' as the family and 'ologit' as the link, I get similar but different coefficients. Stata's ologit command matches his estimates for the mental impairment example on page 279, so it cannot be that there is something which causes the ologit and his ml cumulative logit models to be off all the time. Is there any reason to worry about the slightly different results from time to time? Does anyone know whether SAS computes these models differently? Perhaps I'm missing a nuance about ML estimation vs. some other method of fitting the models? Any hints would be much appreciated, Bill ---------------------------------------------------------------------------- There's nothing about it in the errata (www.stat.ufl.edu/~aa/cda/errors.pdf), but it's possible that the Marginal ML and Marginal GEE columns are transposed. There is an online manuscript by David Clayton in which a GEE estimate for the interaction term is 0.677 with a working correlation matrix estimated from the data, and 0.701 when independence is assumed between the baseline and posttreatment occasions. Those two numbers don't clarify the situation in the textbook, but the manuscript does go on to indicate that Alan Agresti has published at least an empirically weighted least squares (EWLS) estimated model of this dataset. I don't have access to it, but Alan Agresti's paper cited in the manuscript seems to be a survey of applicable methods, so it might also report Marginal ML estimates for this dataset that can be checked against those in his textbook. D. Clayton, Repeated ordinal measurements: a generalised estimating equation approach. 1995. Available at www.mrc-bsu.cam.ac.uk/pub/publications/submitted/david_clayton/ordcat2.ps A. Agresti, A survey of models for repeated ordered categorical response data. _Statistics in Medicine_ 8:1209-24, 1989. (Cited in the online paper above.) Joseph Coveney P.S. Does anyone know what the reception has been of David Clayton's GEE implementation for repeated ordinal data? There is nothing on the method in the Stata directory of his website (he used S for the manuscript); the accompanying readme file states that the manuscript was conditionally accepted by _Applied Statistics_, but abandoned. The manuscript (or an earlier version of it) has been cited in a review article (www.imbs.mu-luebeck.de/reports/paper24.pdf) primarily in the context of how difficult convergence can sometimes be with GEE models of repeated ordered categorical data, but this has been my experience with random effects maximum likelihood methods, too, with ordinal data. * * 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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