Wu Stephanie M, Stephenson Briana Joy K
Harvard T.H. Chan School of Public Health.
Wiley Interdiscip Rev Comput Stat. 2024 Jan-Feb;16(1). doi: 10.1002/wics.1633. Epub 2023 Aug 28.
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, in order 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 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.
由于关于特定社区的数据稀缺，了解这些社区为何以及如何承受不成比例的疾病负担具有挑战性。调查为接触难以触及的人群提供了一条有用途径，因为许多调查专门对研究不足和弱势群体进行过度抽样。当使用调查数据进行分析时，考虑产生这些数据的复杂调查设计很重要，以避免得出有偏差的结论。贝叶斯调查统计领域旨在考虑此类调查设计，同时利用贝叶斯模型的优势，贝叶斯模型可以通过信息借用灵活处理稀疏性，并提供一个连贯的推理框架，以便轻松获得复杂模型和数据类型的方差。出于这些原因，贝叶斯调查方法似乎特别适合健康差异研究，在该研究中，异质性和稀疏性是经常要考虑的因素。本综述讨论了贝叶斯调查方法文献中发现的三种主要方法：1）多级回归和事后分层，2）基于加权伪似然的方法，以及3）合成人群生成。我们讨论了每种方法的优缺点，研究了最近的应用和扩展，并考虑如何利用这些方法来改进人群健康公平性研究。
