A Note on Recurring Misconceptions When Fitting Nonlinear Mixed Models.
Nonlinear mixed-effects (NLME) models are used when analyzing continuous repeated measures data taken on each of a number of individuals where the focus is on characteristics of complex, nonlinear individual change. Challenges with fitting NLME models and interpreting analytic results have been well documented in the statistical literature. However, parameter estimates as well as fitted functions from NLME analyses in recent articles have been misinterpreted, suggesting the need for clarification of these issues before these misconceptions become fact. These misconceptions arise from the choice of popular estimation algorithms, namely, the first-order linearization method (FO) and Gaussian-Hermite quadrature (GHQ) methods, and how these choices necessarily lead to population-average (PA) or subject-specific (SS) interpretations of model parameters, respectively. These estimation approaches also affect the fitted function for the typical individual, the lack-of-fit of individuals' predicted trajectories, and vice versa.
a University of Maryland , College Park.
Jeffrey R
JR
Harring
b University of California , Davis.
Shelley A
SA
Blozis
D000465:Algorithms; D001359:Aviation; D003627:Data Interpretation, Statistical; D006801:Humans; D007858:Learning; D016013:Likelihood Functions; D016014:Linear Models; D017711:Nonlinear Dynamics; D011581:Psychological Tests; D013997:Time Factors
First-order linearization; Gaussian quadrature; nonlinear mixed-effects models; population-average; subject-specific