We have developed a method for marginal analysis of the driving data that motivated this research. The method can be used to deal with correlation within subjects effectively. Two sources of within-subject correlation are considered: variability between subjects and serial correlation. We propose to address the variability between subjects by explicitly adjusting for subject as a fixed effect. To deal with serial correlation, we propose to use separated blocks, with a block-diagonal correlation matrix to account for as much correlation as possible within blocks, and a separation of suitable size to control the correlation between blocks. This procedure may be repeated randomly and the results will be synthesized using the multiple outputation technique. Model-based inference may be used to enhance the performance of the method, especially when the robust variance estimate performs poorly.

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Cheon, Kyeongmi; Thoma, Marie E; Kong, Xiangrong et al. (2014) A mixture of transition models for heterogeneous longitudinal ordinal data: with applications to longitudinal bacterial vaginosis data. Stat Med 33:3204-13
Lai, Yinglei; Albert, Paul S (2014) Identifying multiple change points in a linear mixed effects model. Stat Med 33:1015-28