Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

Data may exhibit dependencies for many reasons. If a patient’s medical condition is measured across several time points, it seems unlikely that these measurements are totally unrelated. Educational ...

This course will discuss the concept of random effects, why they are called random effects and how they are incorporated in the framework of mixed models. The primary focus of the course will be to ...

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson ...

Mixed-effects location scale models represent a powerful statistical framework designed to investigate longitudinal data. By simultaneously modelling the mean trajectories (location) and residual ...