title: Modern statistical modeling approaches for analyzing repeated-measures data.
abstract: BACKGROUND
  Researchers often describe the collection of repeated measurements on each individual in a study design. Advanced statistical methods, namely, mixed and marginal models, are the preferred analytic choices for analyzing this type of data.
  
  
  OBJECTIVE
  The aim was to provide a conceptual understanding of these modeling techniques.
  
  
  METHODS
  An understanding of mixed models and marginal models is provided via a thorough exploration of the methods that have been used historically in the biomedical literature to summarize and make inferences about this type of data. The limitations are discussed, as is work done on expanding the classic linear regression model to account for repeated measurements taken on an individual, leading to the broader mixed-model framework.
  
  
  RESULTS
  A description is provided of a variety of common types of study designs and data structures that can be analyzed using a mixed model and a marginal model.
  
  
  CONCLUSIONS
  This work provides an overview of advanced statistical modeling techniques used for analyzing the many types of correlated .data collected in a research study.
authors.affiliation: College of Nursing, Rutgers University, Newark, New Jersey 07102, USA. matt.hayat@rutgers.edu
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Hayat
authors.forename: Haley
authors.initials: H
authors.lastname: Hedlin
mesh_terms: D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D015999:Multivariate Analysis; D015399:Nursing Research / Q000706:statistics & numerical data*