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Analysis of household effects on longitudinal health outcomes using a joint mean-correlation multilevel model with grouped random effects

Author

Listed:
  • Steele, Fiona
  • Zhang, Siliang
  • Clarke, Paul

Abstract

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.

Suggested Citation

  • Steele, Fiona & Zhang, Siliang & Clarke, Paul, 2026. "Analysis of household effects on longitudinal health outcomes using a joint mean-correlation multilevel model with grouped random effects," LSE Research Online Documents on Economics 138845, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:138845
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    File URL: https://researchonline.lse.ac.uk/id/eprint/138845/
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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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