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Multilevel Regression and Poststratification with Population Margins: Application to HIV Inference

Abstract Number:

2190 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Amy Pitts (1), Maiko Yomogida (1), Angela Aidala (1), Andrew Gelman (1), Qixuan Chen (1)

Institutions:

(1) Columbia University, N/A

Co-Author(s):

Maiko Yomogida  
Columbia University
Angela Aidala  
Columbia University
Andrew Gelman  
Columbia University
Qixuan Chen  
Columbia University

First Author:

Amy Pitts  
Columbia University

Presenting Author:

Amy Pitts  
Columbia University

Abstract Text:

Multilevel Regression and Poststratification (MRP) has gained popularity in survey sampling for population inference. This involves two stages: the first fits a model, regressing the outcome on poststratification variables. The second predicts the outcome using this model and aggregates predictions for population. Existing methods on settings where the joint distribution of the population post-stratifiers is known. However, in practice, such information is not available; instead, we are provided with the margins of the post-stratifiers. Motivated by this challenge, we propose an adapted MRP that models both the survey outcome and the population sizes of subgroups formed by post-stratifiers. Simulations demonstrate that the adapted MRP outperforms methods, with smaller bias, and coverage rate for the 95% probability interval. We apply the adapted MRP to estimate the proportion of viral load and means of mental/physical among with HIV in NYC using the 2020-21 wave of the Community Health Advisory & Information Network survey, in which the collection of was disrupted by the COVID-19 pandemic.

Keywords:

Multilevel Regression and Poststratification (MRP)|Bayesian|Survey Methods|COVID-19| HIV|

Sponsors:

Survey Research Methods Section

Tracks:

Weighting/Variance Estimation

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