Robust transformation mixed-effects models for longitudinal continuous proportional data

Abstract

The authors propose a robust transformation linear mixed-effects model for longitudinal continuous proportional data when some of the subjects exhibit outlying trajectories over time. It becomes troublesome when including or excluding such subjects in the data analysis results in different statistical conclusions. To robustify the longitudinal analysis using the mixed-effects model, they utilize the multivariate t distribution for random effects or/and error terms. Estimation and inference in the proposed model are established and illustrated by a real data example from an ophthalmology study. Simulation studies show a substantial robustness gain by the proposed model in comparison to the mixed-effects model based on Aitchison's logit-normal approach. As a result, the data analysis benefits from the robustness of making consistent conclusions in the presence of influential outliers. © 2009 Statistical Society of Canada.