title: Longitudinal studies 2: Modeling data using multivariate analysis.
abstract: Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors impact upon outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precisions around the point estimates (Confidence Intervals).
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; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D011336:Probability; D012044:Regression Analysis