题目：Efficient estimation for subject-specific effects in longitudinal data using nonnormal linear mixed models.
报告人：Alberta大学的Zhang Peng教授
时间：10月19日下午4点-5点
地点：明德主楼1016
摘要：
We propose a new class of nonnormal linear mixed models that provide an efficient estimation of subject-specific disease progression in the analysis of longitudinal data from the Modification of Diet in Renal Disease (MDRD) trial. We assume a log-gamma distribution for the random effects and provide the maximum likelihood inference for the proposed nonnormal linear mixed model. This method is extended to model associations among subject-specific effects in a multiple characteristics longitudinal study. More reliable estimates of correlations between random effects are obtained using the log-gamma mixed model.To validate the adequacy of the log-gamma assumption versus the usual normality assumption for the random effects, we propose a lack-of-fit test that clearly indicates a better fit for the log-gamma modeling in the analysis of the MDRD data and the glaucoma study.
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