Yland et al. Respond to "Heuristics and Wish Bias".
Yland et al. Respond to "Heuristics and Wish Bias".
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伊兰等人。
DOI：
10.1093/aje/kwac092
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发表时间：
2022
影响因子：
5
通讯作者：
Fox,MatthewP
中科院分区：
文献类型：
--
作者：
Yland,JenniferJ;Wesselink,AmeliaK;Lash,TimothyL;Fox,MatthewP
我们感谢Hamra博士对我们的文章（2）的深思熟虑的评论（1）。哈姆拉博士雄辩地解释了启发式的起源，即无差别错误分类导致对零的偏见，并从理论上解释了为什么它在研究人员中仍然如此受欢迎。他指出，二三十年前我们就知道，这种启发式方法也有例外。在这一点上，我们完全同意。启发式的易错性一直是房间里的大象。在考虑为什么这种启发式方法仍然流行时，哈姆拉博士描述了确认偏差的现象：验证者“倾向于将研究结果投射到一个偏好的图像中”（1，第1497页）。我们同意，确认偏见可能发挥了作用，同时激励“积极”的发现得到发表。通过将研究结果框定在一个偏好的图像中，研究人员无意中给其他潜在的相关偏见蒙上了阴影。在流行病学文章的讨论部分，“错误分类可能是无差别的，因此任何偏倚都是朝着零的”这句话的使用几乎无处不在。通过关注这个问题，调查人员似乎在披露偏见方面是透明的，同时将读者的注意力从那些不“保证”使结果偏向零的偏见上引开。在许多情况下，这种错觉（无论是有意还是无意）可能会为公布提供便利，从而造成证据基础的全面扭曲。总而言之，这种框架隐含地认可了一种价值体系，即高估或假阳性关联一律比低估或假阴性关联更危险，而没有适当考虑谁承担由此产生的扭曲的成本，以及这种不平等的负担是否映射到公共卫生价值和优先事项。不幸的是，许多研究者，包括定量偏倚分析只集中在非微分错误分类。这实际上保证了点估计从零偏移到排除其他偏差。一个更好的方法是考虑同时起作用的多种偏见的综合作用。
We thank Dr. Hamra for a thoughtful commentary (1) on our article (2). Dr. Hamra eloquently explains the origins of the heuristic that nondifferential misclassification results in bias toward the null and theorizes about why it remains so popular among researchers. He points out that we have known for 20 or 30 years that there are exceptions to this heuristic. In this, we completely agree. The fallibility of the heuristic has long been the elephant in the room. In considering why this heuristic remains popular, Dr. Hamra describes the phenomenon of confirmation bias: investigators’“tendency to cast study findings into a preferred image”(1, p. 1497). We agree that confirmation bias likely plays a role alongside incentives for “positive” findings to get published. By framing study findings into a preferred image, investigators inadvertently cast a shadow over other potentially relevant biases. Use of some version of the phrase “misclassification was probably nondifferential, and therefore any bias would be toward the null” has become nearly ubiquitous in the Discussion sections of epidemiologic articles. By focusing on this issue, investigators appear to be transparent in disclosing a bias while drawing the reader’s attention away from biases that are not “guaranteed” to bias one’s results toward the null. In many cases, this illusion (whether intentional or not) may facilitate publication and thereby contribute to overall distortion of the evidence base. Taken together, this framing implicitly endorses a value system that weighs overestimates or false-positive associations as uniformly more dangerous than underestimates or falsenegative associations, without due consideration of who bears what costs from the resulting distortions and whether this unequal burden maps to public health values and priorities.To mitigate this, we argued for the use of quantitative bias analysis in our article (2). Unfortunately, many investigators who include quantitative bias analyses focus solely on nondifferential misclassification. This virtually guarantees a point estimate that is shifted away from the null to the exclusion of other biases. A better approach would be to consider the combined role of multiple biases acting at once.