利用流行病学研究、调查和登记处数据的代表性纯粹风险估计：估计少数亚组的风险
Representative Pure Risk Estimation by Using Data from Epidemiologic Studies, Surveys, and Registries: Estimating Risks for Minority Subgroups
AI总结：
提出两步伪加权程序，通过将事件率后分层至人群率，解决少数群体代表性不足问题，实现高效稳健的纯粹风险估计。
摘要：
代表性风险估计是临床决策的基础。然而，风险通常是从缺乏代表性的流行病学研究中估计的，这些研究通常未能充分代表少数群体。“基于模型”的方法使用人群登记处来提高风险估计的外部有效性，但假设风险比（HR）可以从样本推广到目标有限人群。“伪加权”方法通过使用基于概率的外部调查作为参考来提高研究的代表性，但由于倾向模型设定错误，或由于队列和/或调查中少数群体样本量小导致的可变伪权重，所得估计量可能存在偏差或效率低下。我们提出了一种两步伪加权程序，将伪加权队列中年龄/种族/性别分层的事件率后分层至人群率，以产生高效且稳健的纯粹风险估计（即无竞争事件时的原因别绝对风险）。在开发代表美国全因死亡风险模型时，我们的发现表明，少数群体的HR不可推广，且调查可能缺乏少数群体的事件数量。对事件率进行后分层对于获得少数亚组的可靠风险估计至关重要。
英文摘要：
Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually underrepresent minorities. "Model-based" methods use population registries to improve externally validity of risk estimation but assume hazard ratios (HR) are generalizable from samples to the target finite population. "Pseudoweighting" methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification or inefficient due to variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that poststratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates to produce efficient and robust pure risk estimation (i.e., a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the US, our findings suggest that HRs for minorities are not generalizable, and that surveys can have inadequate numbers of events for minorities. Poststratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups.
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