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Multilevel Regression and Poststratification Using Margins of Poststratifiers: Improving Inference for HIV Health
Amy J Pitts1, Maiko Yomogida2, Angela Aidala2
1Department of Biostatistics, Columbia University, New York, New York, USA.
Statistics in Medicine
|August 12, 2025
Summary
This study introduces an adapted Multilevel Regression and Poststratification (MRP) method for population inference when poststratifier distributions are unknown. The adapted MRP models both survey outcomes and subgroup population sizes, improving accuracy in complex survey data.
Area of Science:
- Statistics
- Survey Methodology
- Public Health
Background:
- Multilevel regression and poststratification (MRP) is a popular technique for population inference from survey samples.
- Traditional MRP methods often require complete population-level information on poststratification variables, which is frequently unavailable.
- Survey data collection can be significantly impacted by real-world events, such as the COVID-19 pandemic.
Purpose of the Study:
- To develop an adapted MRP method for population inference when only marginal distributions of poststratifiers are known.
- To address the challenge of missing joint distributions of poststratification variables in survey data.
- To estimate health outcomes among persons with HIV in New York City using a novel MRP approach.
Main Methods:
- Proposed an adapted MRP approach modeling both the survey outcome and the population sizes of subgroups.
- Utilized Poisson and negative binomial models for subgroup population sizes with few poststratifiers.
- Employed Bayesian additive regression trees for subgroup population sizes with numerous poststratifiers.
- Applied the adapted MRP to estimate viral load suppression and health scale means.
Main Results:
- The adapted MRP method successfully estimated population-level health indicators.
- The study demonstrated the utility of the proposed method in a real-world public health context.
- The method provided estimates despite disruptions in survey data collection due to the COVID-19 pandemic.
Conclusions:
- The adapted MRP offers a viable solution for population inference when joint poststratifier distributions are unknown.
- This method enhances the applicability of MRP in practical survey settings.
- The findings contribute to understanding health disparities among persons with HIV in New York City.
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