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Nonlinear mixed effects models have become a standard platform for analysiswhen data is in the form of continuous and repeated measurements of subjectsfrom a population of interest, while temporal profiles of subjects commonlyfollow a nonlinear tendency. While frequentist analysis of nonlinear mixedeffects models has a long history, Bayesian analysis of the models has receivedcomparatively little attention until the late 1980s due primarily to thetime-consuming nature of Bayesian computation. Since the early 1990s Bayesianapproaches for the models began to emerge to leverage rapid developments incomputing power, and recently, have received significant attention due to (1)superiority to quantify the uncertainty of parameter estimation; (2) utility toincorporate prior knowledge into the models; and (3) flexibility to matchexactly the increasing complexity of scientific research arising from diverseindustrial and academic fields. This review article presents an overview ofmodeling strategies to implement Bayesian approaches for the nonlinear mixedeffects models, ranging from designing a scientific question out of real-lifeproblems to practical computations.

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications