Log-gamma linear-mixed effects models for multiple outcomes with application to a longitudinal glaucoma study
| Autor: | Felipe A. Medeiros, Peng Zhang, Lucie Sharpsten, Dandan Luo, Pengfei Li |
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| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Multivariate statistics genetic structures media_common.quotation_subject Linear model Glaucoma Markov chain Monte Carlo General Medicine Random effects model medicine.disease Shape parameter symbols.namesake Statistics Econometrics symbols medicine sense organs Statistics Probability and Uncertainty Likelihood function Normality media_common Mathematics |
| Zdroj: | Biometrical Journal. 57:766-776 |
| ISSN: | 0323-3847 |
| Popis: | 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 assumption against the usual normality assumption of the random effects, we propose a lack-of-fit test using the profile likelihood function of the shape parameter. We apply this method to data from a prospective observation study, the Diagnostic Innovations in Glaucoma Study, to present a statistically significant association between structural and functional change rates that leads to a better understanding of the progression of glaucoma over time. |
| Databáze: | OpenAIRE |
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