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1 results

  • 11 - Correlated Observations

  • Mitchell H. Katz, NYC Health and Hospitals
  • Book:
    Multivariable Analysis
    Published online:
    09 October 2025
    Print publication:
    23 October 2025, pp 202-224

  • Summary

    Standard statistical analyses assume that each observation (subject) is independent. In other words, the outcomes of different subjects are not correlated. For example, in a longitudinal study, subjects may be assessed repeatedly. Subjects may also be enrolled in established groups or clusters such as families or physician practices. However, when this is not the case, multivariable models that incorporate correlated observations are needed. Common choices are generalized estimating equations and mixed-effects models. Generalized estimating equations are population-averaged models; they estimate the mean difference between the two groups. This is in contrast to mixed-effects models which estimate subject specific differences. Conditional logistic regression is useful with a dichotomous outcome that is measured repeatedly. The Andersen-Gill formation of the proportional hazards model is useful for censored data with outcomes that can over more than once to a subject over time.