Methodological challenges in studying disease processes using observational cohort data

Cohort studies of disease processes deal with events and other outcomes that may occur in individuals following disease onset. The particular goals are often the evaluation of interventions and estimation of the effects of risk factors that may affect the disease course. Models and methods of event history analysis and longitudinal data analysis provide tools for understanding disease processes, but there are numerous challenges in practice. These are related to the complexity of the disease processes and to the difficulty of recruiting representative individuals and acquiring detailed longitudinal data on their disease course. Our objectives here are to describe some of these challenges and to review methods of addressing them. We emphasize the appeal of multistate models as a framework for understanding both disease processes and the processes governing recruitment of individuals for cohort studies and the collection of data. The use of other observational data sources in order to enhance model fitting and analysis is discussed.