Modern statistical modeling approaches for analyzing repeated-measures data

Matthew J. Hayat, Haley Hedlin

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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. Approach: 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. Discussion: This work provides an overview of advanced statistical modeling techniques used for analyzing the many types of collected in a research study.

Original languageEnglish (US)
Pages (from-to)188-194
Number of pages7
JournalNursing research
Issue number3
StatePublished - May 2012


  • correlated data
  • marginal models
  • mixed models
  • repeatedmeasures data

ASJC Scopus subject areas

  • Nursing(all)


Dive into the research topics of 'Modern statistical modeling approaches for analyzing repeated-measures data'. Together they form a unique fingerprint.

Cite this