Diagnostics for repeated measurements in nonlinear mixed effects models

This paper introduces a new non-deletion method which identifies discordant subjects in nonlinear mixed effects models under a self-modeling framework. The new method decomposes the population level residuals into two parts and suggests two-dimensional plots to identify discordant subjects. An observation-wise investigation for alleged discordant subjects is also presented. The performances of the new methods are illustrated with simulation data and two real data examples. The new methods successfully identify the intended or important discordant subjects and observations. In a comparison with the local influence method, the new method reaches a consistent conclusion in a simpler and more efficient manner.