The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Random-Effects Models for Longitudinal Data
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 [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]
Citations
Cites
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Self-citationRecovery of Inter-Block Information when Block Sizes are Unequal
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Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Longitudinal data analysis using generalized linear models
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Bayesian Measures of Model Complexity and Fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy
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Metadata
- 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)
- 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 › Agronomy and Crop Science › 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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