Abstract
In a regression context, the dichotomization of a continuous outcome variable is often motivated by the need to express results in terms of the odds ratio, as a measure of association between the response and one or more risk factors. Starting from the recent work of Moser and Coombs (Stat Med 23:1843–1860, 2004) in this article we explore in a mixed model framework the possibility of obtaining odds ratio estimates from a regression linear model without the need of dichotomizing the response variable. It is shown that the odds ratio estimators derived from a linear mixed model outperform those from a binomial generalized linear mixed model, especially when the data exhibit high levels of heterogeneity.
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Sciandra, M., Muggeo, V.M.R. & Lovison, G. Subject-specific odds ratios in binomial GLMMs with continuous response. Stat Meth Appl 17, 309–320 (2008). https://doi.org/10.1007/s10260-007-0060-x
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DOI: https://doi.org/10.1007/s10260-007-0060-x