Publication type
Journal Article
Authors
Publication date
June 29, 2026
Summary:
Previous cross-sectional research has found correlation in the health outcomes of coresident adults. However, the study of household effects in longitudinal data is challenging due to the complex association structure arising from changes in household membership over time. We propose a ‘grouped’ multilevel model where the groups (called ‘superhouseholds’) are specified to capture changes in household structure. Correlated household random effects are used to capture correlations between households sharing an individual(s), and correlations between household pairs can depend on covariates that describe their relationship. We develop a constrained Markov chain Monte Carlo procedure for model estimation that ensures the group-specific correlation matrices (where dimensions can vary across groups) are positive definite, and implement it as an R package. The performance and robustness of our models are evaluated in a simulation study and then applied in analyses of household and area effects on self-rated physical and mental health in the UK using data from a national household panel survey.
Published in
Journal of the Royal Statistical Society Series A: Statistics in Society
DOI
https://doi.org/10.1093/jrsssa/qnag080
ISSN
09641998
Subjects
Notes
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Online Early
Data availability: Understanding Society data are publicly available to registered users from the UK Data Service at https://datacatalogue.ukdataservice.ac.uk/series/series/2000053#access-data. The relevant study numbers are: SN 6614 for the main datasets and, under Special License Access, SN 7248 for the Lower Layer Super Output Area identifiers. The MCMC algorithm is implemented in an R package (mvregrp) which is available, with documentation and a simulated dataset, from https://github.com/slzhang-fd/mvregrp.
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