NLMIXED Procedure
The NLMIXED procedure fits nonlinear mixed models—that is, models in which both fixed and random effects enter nonlinearly.
These models have a wide variety of applications, two of the most common being pharmacokinetics and overdispersed binomial data.
The following are highlights of the NLMIXED procedure's features:
 enables you to specify a conditional distribution for your data (given the random effects) having either a standard form or a general distribution that you
code using SAS programming statements. The standard forms include the following:
 normal
 binary
 binomial
 gamma
 negative binomial
 Poisson
 fits nonlinear mixed models by maximizing an approximation to the likelihood integrated over the random effects. Different integral approximations are available, the principal
ones being adaptive Gaussian quadrature and a firstorder Taylor series approximation.

 enables you to use the estimated model to construct predictions of arbitrary functions by using empirical Bayes estimates of the random effects
 enables you to specify more than one RANDOM statement in order to fit hierarchical nonlinear mixed models
 enables you to estimate arbitrary functions of the nonrandom parameters and compute their approximate standard errors by using the delta method
 constructs predictions of an expression across all of the observations in the input data set
 accommodates models in which different subjects have identical data
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any output table

For further details see the NLMIXED Procedure
Examples