Re: 1 binary response/person

From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: 1 binary response/person Date: Mon, 17 Sep 2001 17:08:46 -0700 Nick Holford wrote: > > Lewis, > > Would you please confirm that your comments about OMEGA being meaningless are restricted to the single binary response per person case? How exactly do you know they are meaningless? Is your assertion that they are meaningless based on theoretical considerations or on the results of simulation? I am talking about a single binary response per person. It actually applies more widely. Ikuko Yano, Stu and I have a paper in press in JPP ("The need for mixed effect modeling with population dichotomous data") which deals with the matter more extensively. Despite the title, it also p makes some points about continuous 1-observation/person data. > > Are your comments about the precision of estimates from hierarchical vs non hierarchical methods based on (misguided) attempts to estimate OMEGA or in the case where OMEGA is fixed to zero? When OMEGA is set to zero, and there is 1 binary obs/person, then NONMEM does exactly the same thing as standard logistic regression. > > What is your opinion/experience of the meaningfulness of OMEGA estimates for repeated measures binary responses? > I am not familiar with other non-hierarchical methods for logistic regression. Do they exist for repeated measures? If there are repeated measures, then at least in theory, it makes sense to try to estimate a hierarchical model. The to-appear paper I referred to above indicates that even with several observations per person which truly arise from a MEM, a MEM analysis may still not be better than a NPD analysis (i.e. essentially viewing all of the binary observations as independent). > > The key advantages of using NONMEM for binary and other categorical responses is that one is not restricted to estimating parameters of linear (or linearized) models and one can perform joint estimation of PK parameters with the PK predictions driving the model for the binary response. And of course given a hammer everything looks like a nail. This is a feature of NONMEM and not of ML for binary data. As I said above, if you set OMEGA = 0 then NONMEM does what standard logistic regression would do, and, as you point out, makes it more convenient to implement non-linear models for the logistic model parameters. -- _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Bioph. Sci., Med. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)