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From |
"Moran, John (NWAHS)" <John.Moran@nwahs.sa.gov.au> |

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

Subject |
RE: Re: st: RE: Stata 9 announcement |

Date |
Wed, 6 Apr 2005 13:57:11 +0930 |

I read with considerable interest Joseph Coveney's comments below re (P)QL approaches and the other contributions following on from Tom Steichen's e-mail today re Stata 9. For those of us in the bio-medical sphere, categorical outcomes (eg, alive / dead) are _very_ important and , in the general outcomes literature, they are currently associated with large data sets (250000 observations and upwards are not at all unusual, whether these be from administrative or purpose built data-bases). Although we can use "xtlogit", unlike "gllamm" there is no way (?) to access empirical Bayes' estimates whilst using "xtlogit" as with "gllapred" (and "xtreg"). This is somewhat frustrating, as, although it may be useful to demonstrate with "xtlogit" that there is previously undiagnosed "heterogeneity", the literature using multilevel models for (binary) outcomes proceeds to use these EBE to illustrate important aspects of the model (the classic paper, some time ago now, is H. Goldstein and D. J. Spiegelhalter. League tables and their limitations: Statistical issues in the comparisons of institutional performance. Journal of the Royal Statistical Society A 159 (Part3):385-443, 1996.) In Stata, this has also been demonstrated very nicely by Sophia Rabe-Hesketh in the 3rd Edition of "A handbook...." and the current "GLVM" book. Hence I understand the comments by SamL and, perhaps, the sense of disappointment that a module for "non-linear mixed effects models" was not first cab off the rank in Stata 9, although I do not presume to know (i) the reasons for this in official Stata or (ii) what Stata 9 may hold for "gllamm" and the much awaited "Multilevel and Structural Equation Modeling for Continuous, Categorical, and Event Data" (Sophia Rabe-Hesketh, Andrew Pickles, and Anders Skrondal, Stata Press; Expected publication date: June 2005). BTW, I will certainly be purchasing Version 9. John Moran E-mail: john.moran@nwahs.sa.gov.au -----Original Message----- From: Joseph Coveney [mailto:jcoveney@bigplanet.com] Sent: Wednesday, 6 April 2005 12:24 PM To: Statalist Subject: re: Re: st: RE: Stata 9 announcement SamL wrote (excerpted): I am also hoping that David is wrong about stata leaving it to gllamm to work for non-linear mixed models, although this is clearly the short-term situation. Gllamm is a godsend, but it is also very difficult to be sure one is actually estimating what one wants to estimate--I've noticed several queries on statalist that have that flavor. This may be because gllamm is not integrated into stata and thus may not follow stata conventions. I am not sure. Maybe gllamm does follow the conventions closely, but that it is so flexible that it is difficult to document. Whatever the reason, it seems very slow and very opaque. At any rate, even if gllamm runs faster, nothing will substitute for having a module for non-linear mixed models that is actually written and supported by stata. It is my sincere hope that in a few releases (10? 11?) such will be the case. Multi-level modelling of discrete outcomes is pretty mainstream now, for good or ill. It is my hope it will become mainstreamed into stata 10 or stata 11 as well. [edited] ---------------------------------------------------------------------------- ---- There are quasilikelihood approaches to fitting multilevel generalized linear models that are faster than -gllamm- and that might be more suitable than -gllamm- for certain circumstances, but these have their own foibles. In addition, -gllamm- is *much* more flexible and capable than your average penalized quasilikelihood implementation. Even so, quasilikelihood methods should become more accessible with Mata. But -gllamm- is not the general-purpose nonlinear mixed effects model-fitting routing that SAS's PROC NLMIXED is, at least not yet. (You can get marginal nonlinear models now using -ml- with -cluster()-; this kind of approach will become more user-friendly with Stata 9's new -nl, cluster()-.) SamL's comment that the list gets queries by users unsure of what they're getting with -gllamm- might be taken to heart by StataCorp: it's probably better to get -xtmixed- into the hands of Stata users and see how much support is needed with the simpler case before venturing into nonlinear mixed effects models. And after users are accustomed to the steps to be taken when encountering models that settle toward negative variance components (which appear to be constrained to zero by the parameterization that -xtmixed- uses) in order to fit the data, then Stata Corp can proceed to devote resources to embellish -xtmixed- with, say, a bigger smorgasbord of covariance structures (if there's enough user interest--what's already there will likely handle anything I'd ever confront) or denominator degrees of freedom estimations. Joseph Coveney * * 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/ * * 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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