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Intermediate and advanced topics in multilevel logistic regression analysis.

Peter C Austin1,2,3, Juan Merlo4,5

  • 1Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.

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Summary
This summary is machine-generated.

Multilevel logistic regression models are increasingly used in health research. This study introduces complementary analyses to better interpret population-level effects and quantify variance in multilevel models.

Keywords:
clustered datahierarchical modelslogistic regressionmultilevel analysismultilevel models

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Area of Science:

  • Epidemiologic research
  • Health services research
  • Population and public health

Background:

  • Multilevel data are common in health research, with binary outcomes frequently analyzed using multilevel logistic regression.
  • The use of multilevel or hierarchical regression models is rapidly increasing in scientific literature.
  • Analysts often use these models solely to address within-cluster homogeneity, potentially overlooking population-level effects.

Purpose of the Study:

  • To introduce ancillary analyses that complement multilevel logistic regression models.
  • To enable estimation of marginal or population-average effects of covariates, distinct from cluster-specific effects.
  • To provide measures for quantifying variance components, heterogeneity, and model explanatory power.

Main Methods:

  • Description of ancillary analyses for multilevel logistic regression.
  • Introduction of the interval odds ratio and proportion of opposed odds ratios for cluster-level effects.
  • Presentation of the variance partition coefficient, median odds ratio, and an R-squared measure for variance quantification.

Main Results:

  • The proposed methods allow for the estimation of population-average effects alongside cluster-specific effects.
  • New measures quantify the magnitude of contextual effects and the proportion of variation explained by different model components.
  • Application to acute myocardial infarction mortality data illustrates the interpretation of these measures.

Conclusions:

  • Complementary analyses enhance the interpretation of multilevel logistic regression models.
  • These methods provide valuable insights into population-level effects and sources of outcome variation.
  • Quantifying variance and population-average effects is crucial for comprehensive understanding in health research.