Bayesian estimation methods for survey data with potential applications to health disparities research

Abstract

Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard‐to‐reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is important to account for the complex survey design that gave rise to the data, to avoid biased conclusions. The field of Bayesian survey statistics aims to account for such survey design while leveraging the advantages of Bayesian models, which can flexibly handle sparsity through borrowing of information and provide a coherent inferential framework to easily obtain variances for complex models and data types. For these reasons, Bayesian survey methods seem uniquely well‐poised for health disparities research, where heterogeneity and sparsity are frequent considerations. This review discusses three main approaches found in the Bayesian survey methodology literature: (1) multilevel regression and post‐stratification, (2) weighted pseudolikelihood‐based methods, and (3) synthetic population generation. We discuss the advantages and disadvantages of each approach, examine recent applications and extensions, and consider how these approaches may be leveraged to improve research in population health equity.This article is categorized under: Applications of Computational Statistics > Clinical Trials Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Sampling

Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort

“…The hierarchical structure of our models was foundational to the effectiveness of the poststratification step, and to deriving insights that were generalizable to underrepresented groups 65,66,67 . By considering both group-level and individual-level variance, these models enabled us to correct our estimates with exceptional granularity.…”