Ma Weida, MacLehose Richard F, Lash Timothy L, Collin Lindsay J, Tuo Ya, Ahern Thomas P
The Robert Larner, MD College of Medicine at the University of Vermont, Burlington, Vermont, USA.
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Am J Epidemiol. 2024 Sep 5. doi: 10.1093/aje/kwae328.
Mismeasurement of a dichotomous outcome yields an unbiased risk ratio estimate when there are no false positive cases (perfect specificity) and when sensitivity is non-differential with respect to exposure status. In studies where these conditions are expected, quantitative bias analysis may be considered unnecessary. We conducted a simulation study to explore the robustness of this special case to small departures from perfect specificity and stochastic departures from non-differential sensitivity. We observed substantial bias of the risk ratio with specificity values as high at 99.8%. The magnitude of bias increased directly with the true underlying risk ratio and was markedly stronger at lower baseline risk. Stochastic departure from non-differential sensitivity also resulted in substantial bias in most simulated scenarios; downward bias prevailed when sensitivity was higher among unexposed compared with exposed, and upward bias prevailed when sensitivity was higher among exposed compared with unexposed. Our results show that seemingly innocuous departures from perfect specificity (e.g., 0.2%) and from non-differential sensitivity can yield substantial bias of the risk ratio under outcome misclassification. We present a web tool permitting easy exploration of this bias mechanism under user-specifiable study scenarios.
当不存在假阳性病例（完美特异性）且敏感性在暴露状态方面无差异时，对二分结局的错误测量会产生无偏风险比估计值。在预期满足这些条件的研究中，可能认为无需进行定量偏倚分析。我们进行了一项模拟研究，以探讨这种特殊情况对于与完美特异性的微小偏差以及与非差异敏感性的随机偏差的稳健性。我们观察到，特异性值高达99.8%时，风险比仍存在显著偏差。偏差程度直接随真实潜在风险比增加，且在较低基线风险时更为明显。与非差异敏感性的随机偏差在大多数模拟场景中也导致了显著偏差；当未暴露组的敏感性高于暴露组时，主要为向下偏差；当暴露组的敏感性高于未暴露组时，主要为向上偏差。我们的结果表明，与完美特异性（如0.2%）和非差异敏感性的看似无害的偏差，在结局错误分类的情况下会导致风险比出现显著偏差。我们提供了一个网络工具，可在用户指定的研究场景下轻松探究这种偏差机制。
