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Longitudinal data analysisautoregres...

Funatogawa, Ikuko.

Longitudinal data analysisautoregressive linear mixed effects models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Longitudinal data analysisby Ikuko Funatogawa, Takashi Funatogawa.
Reminder of title:	autoregressive linear mixed effects models /
Author:	Funatogawa, Ikuko.
other author:	Funatogawa, Takashi.
Published:	Singapore :Springer Singapore :2018.
Description:	x, 141 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Longitudinal method.
Online resource:	https://doi.org/10.1007/978-981-10-0077-5
ISBN:	9789811000775$q(electronic bk.)

Longitudinal data analysisautoregressive linear mixed effects models /
Funatogawa, Ikuko.

Longitudinal data analysisautoregressive linear mixed effects models /[electronic resource] :by Ikuko Funatogawa, Takashi Funatogawa. - Singapore :Springer Singapore :2018. - x, 141 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

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.

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.

ISBN: 9789811000775$q(electronic bk.)

Standard No.: 10.1007/978-981-10-0077-5doiSubjects--Topical Terms:

191626
Longitudinal method.

LC Class. No.: QA278 / .F863 2018

Dewey Class. No.: 519.535

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520 $a 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.
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