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

Oregon Convention Center 

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.

Multilevel Regression and Poststratification (MRP)

Bayesian

Survey Methods

COVID-19

HIV 

Survey Research Methods Section