From the Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA.
Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.
Epidemiology. 2024 Mar 1;35(2):174-184. doi: 10.1097/EDE.0000000000001711. Epub 2023 Jan 30.
Differential participation in observational cohorts may lead to biased or even reversed estimates. In this article, we describe the potential for differential participation in cohorts studying the etiologic effects of long-term environmental exposures. Such cohorts are prone to differential participation because only those who survived until the start of follow-up and were healthy enough before enrollment will participate, and many environmental exposures are prevalent in the target population and connected to participation via factors such as geography or frailty. The relatively modest effect sizes of most environmental exposures also make any bias induced by differential participation particularly important to understand and account for. We discuss key points to consider for evaluating differential participation and use causal graphs to describe two example mechanisms through which differential participation can occur in health studies of long-term environmental exposures. We use a real-life example, the Canadian Community Health Survey cohort, to illustrate the non-negligible bias due to differential participation. We also demonstrate that implementing a simple washout period may reduce the bias and recover more valid results if the effect of interest is constant over time. Furthermore, we implement simulation scenarios to confirm the plausibility of the two mechanisms causing bias and the utility of the washout method. Since the existence of differential participation can be difficult to diagnose with traditional analytical approaches that calculate a summary effect estimate, we encourage researchers to systematically investigate the presence of time-varying effect estimates and potential spurious patterns (especially in initial periods in the setting of differential participation).
在观察性队列研究中，参与者的差异可能导致有偏甚至反转的估计。在本文中，我们描述了在研究长期环境暴露的病因学效应的队列研究中，参与者可能存在差异的情况。这些队列容易出现参与者的差异，因为只有那些存活到随访开始且在入组前足够健康的人才能参与，而许多环境暴露在目标人群中很普遍，并且通过地理因素或脆弱性等因素与参与相关。大多数环境暴露的效应大小相对较小，因此由参与者差异引起的任何偏差都特别重要，需要理解和考虑。我们讨论了评估参与者差异时需要考虑的要点，并使用因果图描述了两种可能导致长期环境暴露健康研究中出现参与者差异的机制。我们使用一个真实的例子，即加拿大社区健康调查队列，来说明由于参与者差异而导致的不可忽视的偏差。我们还证明，如果感兴趣的效果随时间保持不变，实施简单的洗脱期可以减少偏差并恢复更有效的结果。此外，我们实施了模拟场景，以确认引起偏差的两种机制的合理性和洗脱方法的实用性。由于传统的分析方法计算综合效应估计，难以诊断参与者差异的存在，因此我们鼓励研究人员系统地研究时变效应估计值和潜在的虚假模式的存在（特别是在存在参与者差异的初始阶段）。
