ABSTRACT
Although fairly general, the nonlinear mixed-effects model (7.4.6) of
Chapter 7 is somewhat restrictive in that both the S S random effects and the
within-subject errors are assumed to be normally distributed. Even in the
semi-nonparametric case where we relax our assumption regarding the
normali ty of the random-effects, the conditional model is s t i l l restricted by our
underlying normali ty assumptions on the within-subject errors, e-. Since many
applications dealing wi th repeated measurements involve discrete rather than
continuous outcomes (e.g., binary data, count data and ordinal data),
alternative methods have been proposed for these situations. In this chapter,
we consider a class of generalized nonlinear mixed-effects models which include
both population-averaged ( P A ) and subject-specific (SS) generalized linear
models. These models and the accompanying estimation methods allow us to
analyze repeated measurements data arising from a wide range of applications.