A popular way to represent clustered binary, count, or other data is via the generalized linear
mixed model framework, which accommodates correlation through incorporation of random …

Well-designed computing tools play a fundamental role in the creation and sustenance of
widespread interest in the application of statistical models involving fi xed and random …

Mechanistic modeling greatly benefits from automated pre-and post-processing of model
code and modeling results. While S-ADAPT provides many state-of-the-art parametric …

In the social sciences the prevalence of clustered, correlated, and/or repeatedly measured
data is very common. Modeling such data have classically involved the use of techniques …

Generalized linear mixed models provide a flexible framework for modeling a range of data,
although with non-Gaussian response variables the likelihood cannot be obtained in closed …

This article presents new computational techniques for multivariate longitudinal or clustered
data with missing values. Current methodology for linear mixed-effects models can …

Mixed effects (ME) models inform a vast array of problems in the physical and social
sciences, and are pervasive in meta-analysis. We consider ME models where the random …

Abstract Breslow and Clayton (J Am Stat Assoc 88: 9–25, 1993) was, and still is, a highly
influential paper mobilizing the use of generalized linear mixed models in epidemiology and …

R is a freely available implementation of John Chambers' award-winning S language for
computing with data. It is “Open Source” software for which the user can, if she wishes …

A fully updated edition of this key text on mixed models, focusing on applications in medical
research The application of mixed models is an increasingly popular way of analysing …