Longitudinal Data Analysis: Autoregressive Linear Mixed Effects ModelsSpringer, 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 | |
2 Autoregressive Linear Mixed Effects Models | 27 |
Missing Data and TimeDependent Covariates | 59 |
4 Multivariate Autoregressive Linear Mixed Effects Models | 77 |
Overige edities - Alles bekijken
Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models Ikuko Funatogawa,Takashi Funatogawa Geen voorbeeld beschikbaar - 2019 |
Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models Ikuko Funatogawa,Takashi Funatogawa Geen voorbeeld beschikbaar - 2018 |
Veelvoorkomende woorden en zinsdelen
2llML analysis of longitudinal assumed asymptote autoregressive form autoregressive linear mixed autoregressive models b2 base bbase bint binti bivariate autoregressive linear correlation diagonal Diggle discrete means dose modifications elements equilibria example fixed effects parameters Funatogawa and Funatogawa Gompertz curve inti Kalman filter linear mixed effects linear time trend log-likelihood logistic curve longitudinal data analysis marginal form marginal models maximum likelihood mean structure mean zero measurement error method methylprednisolone mixed effects models ML estimates monomolecular curve multivariate normal distribution nonlinear mixed effects number of parameters oão Obase observation equation PANSS previous response random baseline random errors random intercept random slope regression response changes response level response variable response vector risperidone Section space representation Stat stationary AR(1 statistics Table time-dependent covariate values variance covariance matrix variance covariance parameters variance covariance structure YAsy Yi,t Zibi