JSM 2004 - Toronto

Abstract #300917

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Activity Number: 385
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300917
Title: Analysis of Variance Components in Linear Mixed Effects Models
Author(s): Miao-Yu Tsai*+ and Chu-Hsing Hsiao
Companies: National Taiwan University and National Taiwan University
Address: Division of Biostatistics, Taipei, , Taiwan
Keywords: Bayes factor ; correlation ; GLMM ; longitudinal ; Schwarz criterion ; variance components
Abstract:

This research was originally motivated by a longitudinal study of myopia intervention trial. In addition to testing if one treatment is more efficient than the others, it is also of interest to modeling the observed variance and correlation between two eyes and within repeated measurements per subject. The structures of the variance components can be modeled alone or explained via certain random effects. We consider three different structures for discussion. The first model considers correlated residuals, the second regards the correlation as part of a random effect, and the third model assigns correlated random effects to describe the correlation. We explain the relation between these three models and elucidate the situations where they cannot be differentiated. We also evaluate the errors using both estimates and criteria of model selection when incorrect models are fitted. Finally, we consider simulation studies and the myopia intervention trial for illustrations.

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