  Population pharmacokinetic (PK) modeling methods can be statistically
classified as either parametric or nonparametric (NP). Each classification can
be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this
paper we discuss the nonparametric case using both maximum likelihood and
Bayesian approaches. We present two nonparametric methods for estimating the
unknown joint population distribution of model parameter values in a
pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP
Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a
stick-breaking process to construct a Dirichlet prior. Our objective is to
compare the performance of these two methods using a simulated PK/PD dataset.
Our results showed excellent performance of NPAG and NPB in a realistically
simulated PK study. This simulation allowed us to have benchmarks in the form
of the true population parameters to compare with the estimates produced by the
two methods, while incorporating challenges like unbalanced sample times and
sample numbers as well as the ability to include the covariate of patient
weight. We conclude that both NPML and NPB can be used in realistic PK/PD
population analysis problems. The advantages of one versus the other are
discussed in the paper. NPAG and NPB are implemented in R and freely available
for download within the Pmetrics package from www.lapk.org.
