title: Longitudinal studies 3: Data modeling using standard regression models and extensions.
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. In these circumstances extended models are necessary to handle complexities related to clustered data, and repeated measurements of time-varying predictors and/or outcomes.
authors.affiliation: Division of Nephrology, Department of Medicine, University of Calgary, 1403, 29th St NW (Foothills Medical Centre), Calgary, AB, Canada, T2N 2T9, pravani@ucalgary.ca.
authors.forename: Pietro
authors.initials: P
authors.lastname: Ravani
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Barrett
authors.forename: Patrick S
authors.initials: PS
authors.lastname: Parfrey
mesh_terms: D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D016019:Survival Analysis