Statistical models provide a framework in which to describe the biological process giving
rise to the data of interest. The construction of this model requires balancing adequate …

Much of the research in epidemiology and clinical science is based upon longitudinal
designs which involve repeated measurements of a variable of interest in each of a series of …

Much of the research in epidemiology and clinical science is based upon longitudinal
designs which involve repeated measurements of a variable of interest in each of a series of …

Many common statistical models can be expressed as linear models that incorporate both
fixed effects, which are parameters associated with an entire population or with certain …

Extensions of linear models are very commonly used in the analysis of biological data.
Whereas goodness of fit measures such as the coefficient of determination (R2) or the …

The model generally considered in analysis of covariance has all levels of classification
factors and interactions fixed, and also covariate regression coefficients fixed. Mixed models …

We present recent developments in mixed-effects models relevant to application in
biostatistics. The major focus is on application of mixed-effects models to analysis of …

This article investigates the impact of the normality assumption for random effects on their
estimates in the linear mixed-effects model. It shows that if the distribution of random effects …

A nonlinear mixed-effects model suitable for characterizing repeated measurement data is
described. The model allows dependence of random coefficients on covariate information …

This Tutorial serves as both an approachable theoretical introduction to mixed-effects
modeling and a practical introduction to how to implement mixed-effects models in R. The …