PDF(Pubmed)
Abstract：
UNASSIGNED: To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research.
UNASSIGNED: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.
UNASSIGNED: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.
UNASSIGNED: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.
UNASSIGNED: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.
摘要：
■总结最近关于差异研究中选择偏见的文献，解决描述性或因果关系问题，痴呆症研究的例子。
■定义一个明确的估计，包括目标人群,对于评估泛化偏差或对撞机分层偏差是否对推论构成威胁至关重要。差异研究中的选择偏差可能来自抽样策略，微分夹杂物管道，后续损失，和竞争事件。如果发生竞争事件，可以在不同的假设下估计几个潜在相关的估计，不同的解释。视差的表观幅度可以基于所选择的估计和而实质上不同。如果不是基于已知的抽样方案，随机和观察性研究都可能歪曲健康差异或治疗效果的异质性。
■研究人员最近在与选择偏差相关的概念化和方法方面取得了实质性进展。这一进展将提高描述性和因果健康差异研究的相关性。
■定义一个明确的估计，包括目标人群,对于评估泛化偏差或对撞机分层偏差是否对推论构成威胁至关重要。差异研究中的选择偏差可能来自抽样策略，微分夹杂物管道，后续损失，和竞争事件。如果发生竞争事件，可以在不同的假设下估计几个潜在相关的估计，不同的解释。视差的表观幅度可以基于所选择的估计和而实质上不同。如果不是基于已知的抽样方案，随机和观察性研究都可能歪曲健康差异或治疗效果的异质性。
■研究人员最近在与选择偏差相关的概念化和方法方面取得了实质性进展。这一进展将提高描述性和因果健康差异研究的相关性。
