Examination of Mixed-Effects Models with NonparametricallyGenerated Data
2019
Previous research has shown functional mixed-effects
models and traditional mixed-effects models perform similarly when
recovering mean and individual trajectories (Fine, Suk, &
Grimm, 2019). However, Fine et al. (2019) showed traditional
mixed-effects models were able to more accurately recover the
underlying mean curves compared to functional mixed-effects models.
That project generated data following a parametric structure. This
paper extended previous work and aimed to compare nonlinear
mixed-effects models and functional mixed-effects models on their
ability to recover underlying trajectories which were generated
from an inherently nonparametric process. This paper introduces
readers to nonlinear mixed-effects models and functional
mixed-effects models. A simulation study is then presented where
the mean and random effects structure of the simulated data were
generated using B-splines. The accuracy of recovered curves was
examined under various conditions including sample size, number of
time points per curve, and measurement design. Results showed the
functional mixed-effects models recovered the underlying mean curve
more accurately than the nonlinear mixed-effects models. In
general, the functional mixed-effects models recovered the
underlying individual curves more accurately than the nonlinear
mixed-effects models. Progesterone cycle data from Brumback and
Rice (1998) were then analyzed to demonstrate the utility of both
models. Both models were shown to perform similarly when analyzing
the progesterone data.
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