Multilevel modeling for longitudinal data: concepts and applications

Multilevel Models for Longitudinal Data

Repeated measures and repeated events data have a hierarchical structure which can be analysed using multilevel models. A growth curve model is an example of a multilevel random coefficients model, while a discrete-time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle autoco...

Multilevel and Related Models for Longitudinal Data

Longitudinal data, often called repeated measurements in medicine and panel data in the social sciences, arise when units provide responses on multiple occasions. Such data can be thought of as clustered or two-level data with occasions i at level 1 and units j at level 2. One feature distinguishing longitudinal data from other types of clustered data is the chronological ordering of the respon...

Bayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data

A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...

Medical Informatics: Concepts and Applications

Medical Informatics is a developing body of knowledge concerned with the use of information and communication technology in support of medical research, education and also for promoting health care delivery. The field focuses on the biomedical information, patient data, and also acquisition, storage, retrieval and optimal use of information for problem solving and decision making. The goal of m...

Multilevel Analysis of Grouped and Longitudinal Data