logistic regression: NONMEM LAPLACIAN vs. Gaussian quadrature

From: Leonid Gibiansky leonidg@metrumrg.com Subject: [NMusers] logistic regression: NONMEM LAPLACIAN vs. Gaussian quadrature Date: Mon, 26 Jun 2006 06:36:08 -0400 On the recent PAGE meeting, Dr. Geert Verbeke gave a very interesting tutorial/review of various estimation methods as applied to the logistic regression models ( http://www.page-meeting.org/page/page2006/GeertVerbeke.pdf ). He mentioned that FO method (MQL in his notation) is always biased for these models; FOCE method (PQL) is better but is reasonably good only if you have a lot of observations per subject; and that the methods based on the Gaussian quadrature approach to computation of the integrals are much better than both MQL and PQL. He also mentioned that the methods based on the higher-order Taylor expansions of the integrand (e.g., NONMEM with LAPLACE option; note that NONMEM LAPLACE is NOT THE SAME as Laplace method that was described in the tutorial) are much better than FO/FOCE and comparable with the methods based on Gaussian quadratures. Related question: have anybody compared NONMEM LAPLACE with Gaussian quadrature-based methods for the logistic regression models that we see in PK-PD modeling? Is it possible to give some recommendations when it is safe to use NONMEM with LAPLACE option and when one has to try other packages that implement Gaussian quadrature approach? Any recommendation of those alternative packages (SAS, R/S+)? Thanks Leonid