Interpreting Parameters in the Logistic Regression Model with Random Effects

By K. Larsen, Jørgen Holm Petersen, E. Budtz-Jørgensen, Lars Endahl. 2000

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Citations in the corpus

A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena (2006, methodology): “However, for binary outcomes, the partition of variance between different levels does not have the intuitive interpretation of the linear model. Despite these difficulties several methods have been developed in logistic regression to obtain suitable epidemiological information on area level variance”
Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. (2005, background): “Reporting an 80 percent interval has been suggested (6), and that is reasonable, because it covers a large fraction of the odds ratios.”
Intermediate and advanced topics in multilevel logistic regression analysis (2017, methodology): “[23] for quantifying the magnitude of the effect of clustering when using a multilevel logistic regression model.”
Misidentification of freshwater mussel species (Bivalvia:Unionidae): contributing factors, management implications, and potential solutions (2011, methodology): “To facilitate interpretation, we also calculated scaled odds ratios (SOR) for each fixed-effect observer and species-level predictor variable (Hosmer and Lemeshow 2000) and median odds ratios (MOR) for each random effect (Larsen et al. 2000).”
Income inequality and health: what have we learned so far? (2004, background): “While multilevel models offer great potential in providing such details, researchers need to be cognizant of the challenges in estimating such summary statistics, especially in multilevel logistic models (73, 74), and there is little understanding on what proportion of the total state-attributable v”