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Multilevel regression and poststratification for estimating population quantities from large health studies: a
Marnie Downes1,2, John Carlin3,2,4
1Department of Paediatrics, The University of Melbourne, Parkville, Australia marnie.downes@mcri.edu.au.
Journal of Epidemiology and Community Health
|August 14, 2020
Summary
Multilevel regression and poststratification (MRP) effectively estimates health survey data from non-representative samples. This method offers superior precision and robustness, proving valuable for addressing participation bias in large-scale health surveys.
Area of Science:
- Epidemiology
- Biostatistics
- Survey Methodology
Background:
- Recruiting representative samples for large-scale health surveys is challenging.
- Multilevel regression and poststratification (MRP) is a statistical technique for estimating population characteristics from non-representative samples.
Purpose of the Study:
- To assess the performance of MRP in a US population using a simulation study.
- To compare MRP estimates with those derived from sampling weights (with and without raking).
Main Methods:
- Simulated non-representative datasets from the 2017 US Current Population Survey.
- Estimated state-level prevalence for a dichotomous outcome using MRP and sampling weights.
- Investigated effects of sample size, model misspecification, interactions, and geographic covariates on MRP.
Main Results:
- MRP demonstrated superior performance and precision compared to sampling weights.
- MRP estimates were robust to model misspecification.
- Over-pooling of between-state variation occurred with small sample sizes; a geographic covariate mitigated this.
Conclusions:
- MRP is effective for estimating population quantities in diverse populations, showing generalizability.
- MRP is a valuable tool for mitigating participation bias in large-scale health surveys.
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