Reporting of R 2 Statistics for Mixed-Effects Regression Models

To the Editor We read with interest the article by Andorra et al that evaluated the dynamics of brain volume loss in multiple sclerosis and modeled these variables in mixed-effects regression models as functions of disease duration. The authors report various goodness-of-fit measures of their models, focusing on the coefficient of determination (R2), which ranges from 0 to 1 and represents the proportion of variance in the dependent variable explained by the model. For a model such as ordinary least squares regression, which includes only fixed-effects components, the interpretation of theR2 is intuitive and represents the variance of the dependent variable explained by the independent variable(s). For mixed-effects regression models, there are several variance components, which include both fixed and random effects. Andorra et al cite methods developed by Nakagawa and Schielzeth in calculating their article ’sR2 values. The methods of Nakagawa and Schielzeth defineR2 statistics for mixed-effects models as follows: (1) marginalR2 (variance explained by only fixed effects) and (2) conditionalR2 (variance explained by both fixed and random effects). The marginalR2 is consistent with how most readers will interpret anR2 statistic (using the traditional ordinary least squares interpretation). Notably, Nakagawa and Schielzeth recommend that both marginal and conditionalR2 be reported given that they convey unique and distinctive information.

Source: JAMA Neurology - February 4, 2019 Category: Neurology Source Type: research