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Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
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Nonlinear Mixed Effects (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. However, as the number of parameters and random effects increases, traditional methods for maximizing the marginal likelihood become computationally expensive. T
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