Random effects models for longitudinal data, as discussed in Verbeke, Molenberghs, and Rizopoulos (2010), are essential for analyzing repeated measurements in longitudinal studies. These models account for the correlation between repeated observations from the same subject, which is a key challenge in such data. The paper reviews various models, including linear mixed models for Gaussian data and generalized linear mixed models (GLMMs) for non-Gaussian data. It also addresses issues such as multivariate longitudinal data, time-to-event outcomes, surrogate marker evaluation, and incomplete data. The authors provide case studies, including toenail data, hearing data, liver cirrhosis data, orthodontic growth data, and age-related macular degeneration trial data, to illustrate the application of these models. The paper emphasizes the importance of considering the correlation structure in longitudinal data analysis and discusses different modeling approaches, including generalized estimating equations (GEE) and marginal models. It also highlights the differences between marginal and hierarchical parameter interpretations and the need for appropriate methods to handle missing data and complex correlation structures. The paper concludes with a discussion on multivariate longitudinal data and the use of pairwise model-fitting approaches for high-dimensional applications.Random effects models for longitudinal data, as discussed in Verbeke, Molenberghs, and Rizopoulos (2010), are essential for analyzing repeated measurements in longitudinal studies. These models account for the correlation between repeated observations from the same subject, which is a key challenge in such data. The paper reviews various models, including linear mixed models for Gaussian data and generalized linear mixed models (GLMMs) for non-Gaussian data. It also addresses issues such as multivariate longitudinal data, time-to-event outcomes, surrogate marker evaluation, and incomplete data. The authors provide case studies, including toenail data, hearing data, liver cirrhosis data, orthodontic growth data, and age-related macular degeneration trial data, to illustrate the application of these models. The paper emphasizes the importance of considering the correlation structure in longitudinal data analysis and discusses different modeling approaches, including generalized estimating equations (GEE) and marginal models. It also highlights the differences between marginal and hierarchical parameter interpretations and the need for appropriate methods to handle missing data and complex correlation structures. The paper concludes with a discussion on multivariate longitudinal data and the use of pairwise model-fitting approaches for high-dimensional applications.