Non‐linear models for the analysis of longitudinal data

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Abstract
Given the importance of longitudinal studies in biomedical research, it is not surprising that considerable attention has been given to linear and generalized linear models for the analysis of longitudinal data. A great deal of attention has also been given to non-linear models for repeated measurements, particularly in the field of pharmacokinetics. In this article, a brief overview of non-linear models for the analysis of repeated measures is given. Particular emphasis is placed on mixed-effects non-linear models and on various estimation procedures proposed for such models. Several of these estimation procedures are compared via a simulation study. In addition, simulation is used to investigate the effects of model misspecification, particularly with respect to one's choice of random-effects. A relatively straightforward measure useful in selecting an appropriate set of random effects is investigated and found to perform reasonably well.

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