Gale and colleagues (2017) examined the association between neuroticism and mortality in a large sample (N > 300,000) drawn from the UK Biobank study (Sudlow et al., 2015). They observed that neuroticism was associated with higher all-cause mortality but that following adjustment for self-rated health, neuroticism was associated with lower all-cause mortality. Further analyses stratified on self-rated health suggested that higher neuroticism was associated with reduced mortality only among individuals with fair or poor self-rated health. The authors concluded that “neuroticism becomes protective against mortality from all causes and cancer in people with fair or poor self-rated health” (p. 1355), a finding that generated substantial interest (Macmillan, 2017), reflected in an Altmetric score (at the time of writing) of 416. The availability of very large cohort studies such as UK Biobank in principle allows researchers to identify associations where the absolute effect size may be small but population-level impact considerable (as is the case of the results reported by Gale and colleagues). This is of increasing relevance as cohort studies continue to grow in scale, given that the introduction of even modest bias could lead to robust, but spurious, findings. For instance, when two variables independently influence a third variable and that third variable is conditioned on, this can induce collider bias, which can distort observed associations (Greenland, 2003; Munafò, Tilling, Taylor, Evans, & Davey Smith, 2018). In the case of neuroticism, self-reported health, and mortality, it is plausible that both neuroticism and risk factors for all-cause mortality might influence selfreported health (note that neuroticism could do this by generating less favorable self-reporting of health at any objective level of health status). In that case, conditioning on self-reported health might induce collider bias and generate spurious or distorted associations between neuroticism and both risk factors associated with allcause mortality and all-cause mortality itself. However, if self-reported health were known to influence neuroticism and risk factors for all-cause mortality, then it would be a confounder and should not lead to distorted findings when conditioned on (Fig. 1). We explored this possibility using the same sample drawn from UK Biobank as used by Gale and colleagues. This was done by examining the association between neuroticism and a range of risk factors known to be associated with all-cause mortality, both unstratified and stratified by self-reported health, as stratifying on a collider is one way to condition on it. Specifically, we first analyzed all individuals in the sample (i.e., unstratified) and then repeated our analyses within each of the four different subgroups within the sample, on the basis of self-reported health (i.e., stratified). 774532 PSSXXX10.1177/0956797618774532Richardson et al.Collider Bias and Spurious Associations research-article2019

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对撞机的调节可能会引起虚假关联：研究 Gale 等人的结果。(2017) 支持神经质对人群亚组的健康保护作用？

Gale 及其同事（2017 年）在英国生物银行研究中抽取的大样本（N > 300,000）中检查了神经质与死亡率之间的关联（Sudlow 等人，2015 年）。他们观察到，神经质与较高的全因死亡率相关，但在对自我评估健康状况进行调整后，神经质与较低的全因死亡率相关。对自评健康状况进行分层的进一步分析表明，仅在自评健康状况良好或较差的个体中，较高的神经质与死亡率降低相关。作者得出的结论是，“对于自评健康状况良好或较差的人来说，神经质可以预防各种原因造成的死亡和癌症”（第 1355 页），这一发现引起了人们的极大兴趣（Macmillan，2017 年），反映在 Altmetric 评分中（ 416. 原则上，像英国生物银行这样的大型队列研究的可用性允许研究人员识别绝对效应大小可能很小但人群水平影响相当大的关联（正如所报告的结果的情况）由盖尔和同事撰写）。随着队列研究规模的不断扩大，这一点的相关性越来越大，因为即使引入适度的偏差也可能导致强有力但虚假的发现。例如，当两个变量独立影响第三个变量并且以第三个变量为条件时，这可能会引起碰撞偏差，从而扭曲观察到的关联（Greenland，2003；Munafò、Tilling、Taylor、Evans 和 Davey Smith，2018）。就神经质、自我报告的健康状况和死亡率而言，神经质和全因死亡的危险因素都可能影响自我报告的健康状况（请注意，神经质可以通过在任何时候产生不太有利的健康状况自我报告来做到这一点）健康状况的客观水平）。在这种情况下，以自我报告的健康为条件可能会引起碰撞偏差，并在神经质与全因死亡率和全因死亡率本身相关的风险因素之间产生虚假或扭曲的关联。然而，如果已知自我报告的健康状况会影响神经质和全因死亡的危险因素，那么这将是一个混杂因素，并且在条件化时不应导致扭曲的结果（图1）。我们使用 Gale 及其同事使用的从英国生物银行抽取的相同样本探索了这种可能性。这是通过检查神经质与一系列已知与全因死亡率相关的风险因素之间的关联来完成的，这些风险因素包括未分层的和按自我报告的健康状况分层的，因为对撞机进行分层是对其进行调节的一种方法。具体来说，我们首先分析样本中的所有个体（即未分层），然后根据自我报告的健康状况（即分层）在样本内的四个不同亚组中重复我们的分析。774532 PSSXXX10.1177/0956797618774532理查森等人。