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横断面研究能否有助于因果推断?这要看情况。

Can Cross-Sectional Studies Contribute to Causal Inference? It Depends.

作者信息

Savitz David A, Wellenius Gregory A

出版信息

Am J Epidemiol. 2023 Apr 6;192(4):514-516. doi: 10.1093/aje/kwac037.

Abstract

Cross-sectional studies-often defined as those in which exposure and outcome are assessed at the same point in time-are frequently viewed as minimally informative for causal inference. While cross-sectional studies may be susceptible to reverse causality, may be limited to assessment of disease prevalence rather than incidence, or may only provide estimates of current rather than past exposures, not all cross-sectional studies suffer these limitations. Moreover, none of these concerns are unique to or inherent in the structure of a cross-sectional study. Regardless of when exposure and disease were ascertained relative to one another, a cross-sectional study may provide insights into the causal effects of exposure on disease incidence. Simply labeling a study as "cross-sectional" and assuming that 1 or more of these limitations exist and are materially important fails to recognize the need for a more nuanced assessment and risks discarding evidence that may be useful in assessing causal relationships.

摘要

横断面研究(通常定义为在同一时间点评估暴露因素和结局的研究)常常被认为对于因果推断的信息量极小。虽然横断面研究可能易受反向因果关系的影响,可能局限于疾病患病率而非发病率的评估,或者可能仅提供当前而非过去暴露情况的估计值,但并非所有横断面研究都存在这些局限性。此外,这些问题并非横断面研究结构所特有或固有的。无论暴露因素和疾病是何时相互确定的,横断面研究都可能提供有关暴露因素对疾病发病率因果效应的见解。仅仅将一项研究标记为“横断面研究”,并假定存在这些局限性中的一项或多项且具有重大意义,就无法认识到需要进行更细致入微的评估,并且有丢弃可能有助于评估因果关系的证据的风险。

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