Log-gamma linear-mixed effects models for multiple outcomes with application to a longitudinal glaucoma study.

ABSTRACT: Glaucoma is a progressive disease due to damage in the optic nerve with associated functional losses. Although the relationship between structural and functional progression in glaucoma is well established, there is disagreement on how this association evolves over time. In addressing this issue, we propose a new class of non-Gaussian linear-mixed models to estimate the correlations among subject-specific effects in multivariate longitudinal studies with a skewed distribution of random effects, to be used in a study of glaucoma. This class provides an efficient estimation of subject-specific effects by modeling the skewed random effects through the log-gamma distribution. It also provides more reliable estimates of the correlations between the random effects. To validate the log-gamma assu

SUBMITTER: Zhang P 

PROVIDER: S-EPMC4558301 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature