BACKGROUND When estimating the effect of time-varying exposures on longer-term outcomes, the assumption of conditional exchangeability or no uncontrolled confounding extends beyond baseline confounding to include time-varying confounding. We illustrate the structures and magnitude of uncontrolled time-varying confounding in exposure effect estimates obtained from g-computation when sequential conditional exchangeability is violated. METHODS We used directed acyclic graphs (DAGs) to depict time-varying uncontrolled confounding. We performed simulations and used g-computation to quantify the effects of each time-varying exposure for each DAG type. Models adjusting all time-varying confounders were considered the true (bias-adjusted) estimate. The exclusion of time-varying uncontrolled confounders represented the biased effect estimate and an unmet 'no uncontrolled confounding' assumption. True and biased estimates were compared across DAGs, with different magnitudes of uncontrolled confounding. RESULTS Time-varying uncontrolled confounding can present in several scenarios, including relationships into subsequently measured exposure(s), outcome, unmeasured confounder(s) and other measured confounder(s). In simulations, effect estimates obtained from g-computation were more biased in DAGs when the uncontrolled confounders were directly related to the outcome. Complex DAGs that included relationships between uncontrolled confounders and other variables and relationships where exposures caused uncontrolled confounders at the next time point resulted in the most biased effect estimates. In these complex DAGs, excluding uncontrolled confounders affected the multiple effect estimates. CONCLUSIONS Time-varying uncontrolled confounding has the potential to substantially impact observed effect estimates. Given the importance of longitudinal studies in advising public health, the impact of time-varying uncontrolled confounding warrants more recognition and evaluation using quantitative bias analysis.

中文翻译：

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使用 g 计算分析模拟队列数据中时变不受控混杂的结构和大小的调查。

背景 在估计时变暴露对长期结果的影响时，条件可交换性或没有不受控制的混杂因素的假设超出了基线混杂因素，包括时变混杂因素。我们说明了当顺序条件可交换性被违反时，从 g 计算获得的暴露效应估计中不受控制的时变混杂的结构和大小。方法我们使用有向无环图（DAG）来描述随时间变化的不受控制的混杂因素。我们进行了模拟并使用 g 计算来量化每种 DAG 类型的每次变化暴露的影响。调整所有时变混杂因素的模型被认为是真实的（偏差调整的）估计。排除随时间变化的不受控制的混杂因素代表了有偏差的效应估计和未满足的“没有不受控制的混杂因素”假设。跨 DAG 比较了真实估计和有偏估计，以及不同程度的不受控制的混杂因素。结果随时间变化的不受控制的混杂因素可能出现在多种情况下，包括与随后测量的暴露、结果、未测量的混杂因素和其他测量的混杂因素的关系。在模拟中，当不受控制的混杂因素与结果直接相关时，从 g 计算获得的效应估计在 DAG 中偏差更大。复杂的 DAG 包括不受控制的混杂因素和其他变量之间的关系，以及暴露在下一个时间点导致不受控制的混杂因素之间的关系，从而导致最有偏差的效应估计。在这些复杂的 DAG 中，排除不受控制的混杂因素会影响多重效应估计。结论 时变的不受控制的混杂因素有可能对观察到的效果估计产生重大影响。鉴于纵向研究在为公共卫生提供建议方面的重要性，时变的不受控制的混杂因素的影响值得使用定量偏差分析进行更多的认识和评估。