Weisskopf Marc G, Sparrow David, Hu Howard, Power Melinda C
Department of Epidemiology and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Environ Health Perspect. 2015 Nov;123(11):1113-22. doi: 10.1289/ehp.1408888. Epub 2015 May 8.
The process of creating a cohort or cohort substudy may induce misleading exposure-health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk.
We aimed to demonstrate how such biases of the exposure-health effect association arise and how one may mitigate them.
We used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).
Analyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively.
Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls.
Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure-health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ Health Perspect 123:1113-1122; http://dx.doi.org/10.1289/ehp.1408888.
创建队列或队列子研究的过程可能会通过对撞分层偏倚（即选择偏倚）或因对中间变量进行条件设定而导致的偏倚，产生具有误导性的暴露-健康效应关联。环境风险因素的研究可能尤其容易受到影响。
我们旨在说明暴露-健康效应关联中的此类偏倚是如何产生的，以及如何减轻这些偏倚。
我们使用有向无环图以及规范衰老研究（NAS）中835名白人男性的骨铅与死亡率（全因、心血管疾病和缺血性心脏病）的例子，来说明与纳入NAS和骨铅子研究相关的潜在偏倚。然后，我们应用一些方法（调整、限制和失访逆概率加权），在使用Cox比例风险模型估计调整后的风险比（HR）和95%置信区间（CI）的分析中减轻这些偏倚。
在对所有男性的骨铅测量年龄、吸烟和教育程度进行调整的分析中，髌骨铅含量最高三分位数与最低三分位数相比，全因、心血管疾病和缺血性心脏病死亡率的HR（95%CI）分别为1.34（0.90，2.00）、1.46（0.86，2.48）和2.01（0.86，4.68）。在应用减轻偏倚的方法后，分析的637名男性中的HR（95%CI）分别为1.86（1.12，3.09）、2.47（1.23，4.96）和5.20（1.61，16.8）。
仔细关注观察数据的潜在结构对于识别潜在偏倚及其减轻方法至关重要。了解影响初始研究参与和随访失访的因素至关重要。招募基于人群的样本并在潜在的暴露相关健康效应出现之前，在较年轻的年龄纳入参与者，有助于减少这些潜在的陷阱。
Weisskopf MG, Sparrow D, Hu H, Power MC. 2015.队列研究中因选择导致的暴露-健康效应估计有偏：环境研究是否尤其危险？《环境健康展望》123:1113 - 1122；http://dx.doi.org/10.1289/ehp.1408888 。
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