Random-Effects Models for Longitudinal Data

Laird, Nan M.; Ware, James H.

doi:10.2307/2529876

Cited by 8,923Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisAgriculture, Soil, Plant Science

Random-Effects Models for Longitudinal Data

Laird, Nan M., Ware, James H.·1982·Biometrics

DOI 10.2307/2529876

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Abstract

Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a distribution specified at the second stage. A general family of models is discussed, which includes both growth models and repeated-measures models as special cases. A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed. Two examples are taken from a current epidemiological study of the health effects of air pollution.

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The linear mixed-effect model contains both fixed and random effects, allowing for both within-participant and between-participants variations over repeated measurements.
“The linear mixed-effect model contains both fixed and random effects, allowing for both within-participant and between-participants variations over repeated measurements [39].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [39]

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Authors: Laird, N. M., & Ware, J. H.
ORCID: Laird, Nan M.
Venue: Biometrics
Publisher: JSTOR
Published: 1982-12
Volume: 38
Issue: 4
Pages: 963
ISSN: 0006-341X (ISSN-L)
DOI: 10.2307/2529876
OpenAlex: W2082246284
Cited by: 8,923
References: 17
Citations by year: 2026: 289 · 2025: 221 · 2024: 257 · 2023: 237 · 2022: 295 · 2021: 395 · 2020: 304 · 2019: 310 · 2018: 311 · 2017: 335 · 2016: 310 · 2015: 352 · 2014: 374 · 2013: 346 · 2012: 424
FWCI: 8.53
Type: journal article
Language: it
Open access: closed
Locations: Biometrics (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Agriculture, Soil, Plant Science
Keywords: random effects models, longitudinal data, two-stage model, class imbalance, growth models, repeated measures models, empirical Bayes, maximum likelihood estimation, expectation-maximization algorithm, covariance structure, air pollution health effects
MeSH terms: Air Pollution, Body Height, Child, Female, Forced Expiratory Flow Rates, Humans, Longitudinal Studies, Male, Models, Theoretical, Statistics as Topic

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