Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models

Voorkant
Springer, 4 feb 2019 - 141 pagina's
This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.
 

Inhoudsopgave

1 Longitudinal Data and Linear Mixed Effects Models
1
2 Autoregressive Linear Mixed Effects Models
27
Missing Data and TimeDependent Covariates
59
4 Multivariate Autoregressive Linear Mixed Effects Models
77
5 Nonlinear Mixed Effects Models Growth Curves and Autoregressive Linear Mixed Effects Models
99
6 State Space Representations of Autoregressive Linear Mixed Effects Models
119
Index
139
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Over de auteur (2019)

Ikuko Funatogawa, The Institute of Statistical Mathematics
Takashi Funatogawa, Chugai Pharmaceutical Co. Ltd.

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