Overdispersion and correlation are two features often encountered when modeling non-Gaussian dependent data, usually as a function of known covariates. Methods that ignore the presence of these phenomena are often in jeopardy of leading to biased assessment of covariate effects. The...

The shared-parameter model and its so-called hierarchical or random-effects extension are widely used joint modeling approaches for a combination of longitudinal continuous, binary, count, missing, and survival outcomes that naturally occurs in many clinical and other studies. A random effect is...

Large data sets, either coming from a large number of independent replications, or because of hierarchies in the data with large numbers of within-unit replication, may pose challenges to the data analyst up to the point of making conventional inferential methods, such as maximum likelihood,...