Tree-Structured Mixed-Effects Regression Modeling for Longitudinal Data

2014 ◽  
Vol 23 (3) ◽  
pp. 740-760 ◽  
Author(s):  
Soo-Heang Eo ◽  
HyungJun Cho
Keyword(s):  
Longitudinal Data ◽  
Mixed Effects ◽  
Regression Modeling

scholarly journals Joint mixed-effects models for causal inference with longitudinal data

2017 ◽  
Vol 37 (5) ◽  
pp. 829-846 ◽  
Author(s):  
Michelle Shardell ◽  
Luigi Ferrucci
Keyword(s):  
Longitudinal Data ◽  
Causal Inference ◽  
Mixed Effects ◽  
Mixed Effects Models

Lag as moderator meta-analysis: A methodological approach for synthesizing longitudinal data

2018 ◽  
Vol 43 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Noel A. Card
Keyword(s):  
Longitudinal Data ◽  
Human Development ◽  
Longitudinal Research ◽  
Meta Analysis ◽  
Methodological Approach ◽  
Mixed Effects ◽  
Effect Sizes ◽  
Time Lags ◽  
Research Findings ◽  
The Impact

Longitudinal data are common and essential to understanding human development. This paper introduces an approach to synthesizing longitudinal research findings called lag as moderator meta-analysis (LAMMA). This approach capitalizes on between-study variability in time lags studied in order to identify the impact of lag on estimates of stability and longitudinal prediction. The paper introduces linear, nonlinear, and mixed-effects approaches to LAMMA, and presents an illustrative example (with syntax and annotated output available as online Supplementary Materials). Several extensions of the basic LAMMA are considered, including artifact correction, multiple effect sizes from studies, and incorporating age as a predictor. It is hoped that LAMMA provides a framework for synthesizing longitudinal data to promote greater accumulation of knowledge in developmental science.

The Hub

2019 ◽  
pp. 141-181
Author(s):  
James N. Stanford
Keyword(s):  
Social Class ◽  
New England ◽  
Social Factors ◽  
Mixed Effects ◽  
Regression Modeling ◽  
Linear Mixed Effects ◽  
Eastern Massachusetts ◽  
Over Time

This is the first of two chapters (Chapters 6 and 7) that analyze fieldwork results in eastern Massachusetts. This chapter analyzes the eastern Massachusetts “Hub” region as a whole, providing a statistical overview of speakers interviewed in the Dartmouth-based fieldwork in this area. It examines the results in terms of major traditional Eastern New England dialect features, including Linear Mixed Effects regression modeling in terms of phonetic environments and social factors like age, gender, social class, and ethnicity. The chapter also plots these dialect features in terms of speakers’ birth year and other factors, showing how these features are changing over time.

scholarly journals Mixed-Effects Tobit Joint Models for Longitudinal Data with Skewness, Detection Limits, and Measurement Errors

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Getachew A. Dagne ◽  
Yangxin Huang
Keyword(s):  
Detection Limit ◽  
Longitudinal Data ◽  
Normal Distribution ◽  
Measurement Errors ◽  
Real Data ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Joint Models ◽  
Departure From Normality ◽  
Invalid Inference

Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. In this paper, we jointly investigate a skew-tNLME Tobit model for response (with left censoring) process and a skew-tnonparametric mixed-effects model for covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.

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