使用 E 值解决观察性研究中的混杂问题——文献的实证评估。

Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature.

机构信息

Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA.

Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

出版信息

Int J Epidemiol. 2020 Oct 1;49(5):1482-1494. doi: 10.1093/ije/dyz261.

DOI:10.1093/ije/dyz261

PMID:31930286

Abstract

BACKGROUND

E-values are a recently introduced approach to evaluate confounding in observational studies. We aimed to empirically assess the current use of E-values in published literature.

METHODS

We conducted a systematic literature search for all publications, published up till the end of 2018, which cited at least one of two inceptive E-value papers and presented E-values for original data. For these case publications we identified control publications, matched by journal and issue, where the authors had not calculated E-values.

RESULTS

In total, 87 papers presented 516 E-values. Of the 87 papers, 14 concluded that residual confounding likely threatens at least some of the main conclusions. Seven of these 14 named potential uncontrolled confounders. 19 of 87 papers related E-value magnitudes to expected strengths of field-specific confounders. The median E-value was 1.88, 1.82, and 2.02 for the 43, 348, and 125 E-values where confounding was felt likely to affect the results, unlikely to affect the results, or not commented upon, respectively. The 69 case-control publication pairs dealt with effect sizes of similar magnitude. Of 69 control publications, 52 did not comment on unmeasured confounding and 44/69 case publications concluded that confounding was unlikely to affect study conclusions.

CONCLUSIONS

Few papers using E-values conclude that confounding threatens their results, and their E-values overlap in magnitude with those of papers acknowledging susceptibility to confounding. Facile automation in calculating E-values may compound the already poor handling of confounding. E-values should not be a substitute for careful consideration of potential sources of unmeasured confounding. If used, they should be interpreted in the context of expected confounding in specific fields.

摘要

背景

E 值是一种用于评估观察性研究中混杂因素的新方法。我们旨在对已发表文献中 E 值的使用情况进行实证评估。

方法

我们系统地检索了截至 2018 年底引用了两篇初始 E 值论文中的至少一篇并为原始数据呈现了 E 值的所有出版物。对于这些案例出版物，我们确定了与期刊和期号相匹配的对照出版物，其中作者没有计算 E 值。

结果

共有 87 篇论文呈现了 516 个 E 值。在这 87 篇论文中，有 14 篇得出结论认为，残留混杂因素可能至少威胁到部分主要结论。这 14 篇论文中的 7 篇提到了潜在的未控制混杂因素。87 篇论文中有 19 篇将 E 值大小与特定领域预期混杂因素的强度相关联。在 43 个、348 个和 125 个 E 值中，分别有 43 个、348 个和 125 个认为混杂因素可能影响结果、不太可能影响结果或未发表评论，E 值中位数分别为 1.88、1.82 和 2.02。69 对病例对照出版物处理了相似大小的效应量。在 69 篇对照出版物中，有 52 篇未评论未测量的混杂因素，44/69 篇病例报告认为混杂因素不太可能影响研究结论。