title: Modeling longitudinal data, I: principles of 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 on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component 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 precision around the point estimates (confidence intervals).
authors.affiliation: Divisione di Neprologia, Azienda Instituti, Ospitalieri di Cremona, Cremona, Italy.
authors.forename: Pietro
authors.initials: P
authors.lastname: Ravani
authors.forename: Brendan
authors.initials: B
authors.lastname: Barrett
authors.forename: Patrick
authors.initials: P
authors.lastname: Parfrey
mesh_terms: D006801:Humans; D008137:Longitudinal Studies; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D012107:Research Design / Q000706:statistics & numerical data*; D013223:Statistics as Topic