The analysis of data with outcomes measured repeatedly on each subject has experienced several transforming developments in the last twenty years. This monograph presents a unified treatment of modern methods for longitudinal and/or correlated data that have developed during this period. The basic approach that we take to modeling longitudinal data is to extend familiar univariate regression models to multivariate or correlated outcomes. We deal with linear models for measured data and generalized linear models for binary and count data. We show how methods can accommodate missing outcomes and/or unbalanced designs. Both likelihood and moment methods of estimation are covered, as are random effects approaches to data modeling and parameter estimation.

The monograph assumes that the reader has a solid foundation in statistical inference, linear and generalized linear regression models, and a basic knowledge of multivariate methods. It is appropriate for second year doctoral students or postdoctoral fellows in Statistics/Biostatistics as well as researchers or faculty interested in learning about the field.