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.

Describes a new analytical approach for longitudinal data, autoregressive linear mixed effects models, in which dynamic models are induced by the auto-regression term Provides state space representation of autoregressive linear mixed models with the modified Kalman filter for the calculation of log likelihoods Is written in plain English dealing not only with topics for those in medical fields but that is also understandable for researchers in other disciplines

Autorentext

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

Inhalt
Chapter 1. Linear mixed effects model.- Chapter 2. Autoregressive linear mixed effects model.- Chapter 3. Bivariate longitudinal data.- Chapter 4. State-space representation.- Chapter 5. Missing data, time dependent covariate.- Chapter 6. Pretest-Posttest data.