In many clinical trials, in order to characterize the safety profile of a subject with a given treatment, multiple measurements are taken over time. Mostly, measurements taken from the same subject are not independent. The types of model for data analysis highly depend on the nature and measurement scale of the outcome variable. In this book, the two model families: marginal, and random effect models that take the correlation among measurements of the same subject into account were briefly discussed using real datasets. In case of marginal models the correlated nature of the data is taken into account inside the estimating equation, while for random effects model it is done through the random effect part. In the random effect approach the goal is to determine subject-specific changes over the courses of the study, while in the marginal model the emphasis is to determine the overall change. Joint modeling approaches that used to model different outcomes jointly, and model selection and diagnosis measures were discussed briefly. Moreover, SAS codes used in the analysis for each model type were included in book.