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Obtaining population-based estimates for survey data using Bayesian hierarchical models with poststratification.

Yunxuan Zhang1, Thomas M Gill2, Karen Bandeen-Roche3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States.

American Journal of Epidemiology
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

Researchers can now combine National Health and Aging Trends Study (NHATS) cohorts using Bayesian models. This method provides accurate population-based estimates, enabling larger sample sizes for health and aging research.

Keywords:
Bayesian methodsNHATSsurvey weights

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Area of Science:

  • Gerontology
  • Biostatistics
  • Epidemiology

Background:

  • Large-scale surveys like the National Health and Aging Trends Study (NHATS) are crucial for aging research.
  • Combining data from multiple NHATS cohorts can increase statistical power.
  • Existing methods prevent combining NHATS cohorts (2011, 2015) while preserving sample weights.

Purpose of the Study:

  • To develop and validate a Bayesian hierarchical modeling approach for combining NHATS cohorts.
  • To generate population-based frailty estimates from combined NHATS data.
  • To enhance the utility of NHATS for researchers requiring larger sample sizes.

Main Methods:

  • Bayesian hierarchical models with poststratification were employed.
  • Prevalence estimates of frailty were compared between the Bayesian approach and weighted NHATS estimates (2011, 2015).
  • A combined analytical dataset was created without participant overlap for Bayesian estimation.

Main Results:

  • Bayesian model estimates closely matched the weighted NHATS estimates.
  • The validated Bayesian strategy successfully combined NHATS cohorts.
  • Population-based frailty estimates were generated for the combined cohort.

Conclusions:

  • Bayesian hierarchical models with poststratification offer a valid method for combining NHATS cohorts.
  • This approach allows for the generation of population-based estimates from merged datasets.
  • The enhanced analytical capabilities will facilitate research on aging and health using larger sample sizes.