Linear Mixed Models I

  • Brian Everitt
  • Sophia Rabe-Hesketh
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices. In both types of data, we cannot assume that observations on the same subject (or cluster) are independent. Standard methods of analysis such as ANOVA or multiple regression, which assume that observations are independent, are therefore not valid for clustered data. Fortunately, these methods can be extended by explicitely modeling the covariances among observations within a cluster. In this chapter, we discuss how this can be done using linear mixed models.

Keywords

Linear Mixed Model Random Effect Model Applied Pressure Peak Expiratory Flow Rate Random Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Brian Everitt
    • 1
  • Sophia Rabe-Hesketh
    • 1
  1. 1.Biostatistics and Computing DepartmentInstitute of PsychiatryLondonUK

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