Modeling Longitudinal Data

Abstract

In longitudinal studies, the relationship between exposure and disease can be measured once or multiple times while participants are monitored over time. Traditional regression techniques are used to model outcome data when each epidemiological unit is observed once. These models include generalized linear models for quantitative continuous, discrete, or qualitative outcome responses and models for time-to-event data. When data come from the same subjects or group of subjects, observations are not independent and the underlying correlation needs to be addressed in the analysis. Under these circumstances, extended models are necessary to handle complexities related to clustered data and repeated measurements of time-varying predictors or outcomes.