Longitudinal experiments often involve multiple outcomes measured repeatedly within a set
of study participants. While many questions can be answered by modeling the various …

Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of
survival data, illustrated using a wide range of examples from biomedical research. Written …

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and
associations into models for survival data. In its simplest form, frailty is an unobserved …

The level of surveillance and preparedness against epidemics varies across countries,
resulting in different responses to outbreaks. When conducting an in-depth analysis of …

A family of generalized linear models for repeated measures with normal and conjugate
random effects: Calculation details. In Section A, generic approximate calculations are …

Renewable generation forecasting has become an essential element of power system
management because of the ever-increasing share of renewable power fed into the grid …

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R,
SAS, and BUGS provides the reader with a practical introduction into the analysis of interval …

In this paper, we propose a flexible “two-part” random effects model (Olsen and Schafer,
2001; Tooze et al., 2002) for correlated medical cost data. Typically, medical cost data are …

Estimation in generalized linear mixed models (GLMMs) is often based on maximum
likelihood theory, assuming that the underlying probability model is correctly specified …

In many longitudinal studies, we observe two correlated processes: a repeated measures
process and a recurrent events process, both subject to a dependent terminal event. For …