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An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes
Author(s) -
Funatogawa Ikuko,
Funatogawa Takashi,
Ohashi Yasuo
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2670
Subject(s) - asymptote , autoregressive model , random effects model , longitudinal data , mixed model , linear model , missing data , star model , generalized linear mixed model , mathematics , statistics , econometrics , computer science , time series , autoregressive integrated moving average , mathematical analysis , data mining , medicine , meta analysis
In longitudinal data, a continuous response sometimes shows a profile approaching an asymptote. For such data, we propose a new class of models, autoregressive linear mixed effects models in which the current response is regressed on the previous response, fixed effects, and random effects. Asymptotes can shift depending on treatment groups, individuals, and so on, and can be modelled by fixed and random effects. We also propose error structures that are useful in practice. The estimation methods of linear mixed effects models can be used as long as there is no intermittent missing. Copyright © 2006 John Wiley & Sons, Ltd.