具有递归和半竞争事件的时间变化动作的新型g计算算法
Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events
AI总结：
本文提出新型g计算算法，同时处理半竞争事件和时间变化动作，用于评估吸烟预防对高血压的影响。
摘要：
背景：流行病学的核心方面是确定潜在公共卫生干预措施随时间变化的影响。在长期随访期间，流行病学家可能需要考虑半竞争事件，其中终端事件，如死亡，会阻止非终端事件，如高血压。当研究时间变化的干预措施或动作时，时间变化的混杂因素会带来额外的挑战。现有方法并未同时解决半竞争事件和时间变化混杂因素。\n\n方法：我们提出了两种新的g计算算法，用于具有半竞争事件和时间变化动作的因果效应。为了探讨我们新g计算估计器的性能，我们进行了蒙特卡洛模拟研究。然后，我们将我们的估计器应用于研究青年和中年时期戒烟预防对现有高血压的影响，使用美国青少年至成人健康纵向研究（Waves III至VI）的数据。\n\n结果：我们的模拟显示，新g计算估计器的偏差很小，置信区间覆盖适当。在不同样本规模下，它们的表现优于现有替代估计器。在示例应用中，新估计器发现，如果在随访期间完全防止吸烟，那么中年时期高血压的患病率和死亡风险会略有降低，与观察到的吸烟模式相比。\n\n结论：随着长期队列年龄的增长，研究样本中的死亡将成为研究与年龄相关的结果、人生轨迹分析以及慢性病发展研究日益关注的问题。我们新提出的g计算估计器提供了一种同时解决的方法。
英文摘要：
Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to consider semi-competing events, in which a terminal event, like death, precludes a non-terminal event, like hypertension. Time-varying confounding poses an additional challenge when studying time-varying interventions or actions. Existing methods do not simultaneously address semi- competing events and time-varying confounding. Methods: We propose two novel g-computation algorithms for causal effects with semi- competing events and time-varying actions. To explore performance of our novel g-computation estimators, we conducted a Monte Carlo simulation study. We then applied our estimator to investigate how cigarette smoking prevention throughout young and middle adulthood might impact prevalent hypertension using data from Waves III (aged 18-26 years) - VI (aged 39-51 years) of the National Longitudinal Study of Adolescent to Adult Health. Results: Our simulations show that the novel g-computation estimators had little bias and appropriate confidence interval coverage. They outperformed existing alternative estimators across sample sizes. In the illustrative application, the novel estimator identified a small reduction in prevalence of hypertension and risk of death in midlife had all cigarette smoking been prevented across follow-up compared to the observed smoking patterns. Conclusion: As long-running cohorts progress in age, death within the study sample will become an increasing concern for studies of aging-related outcomes, life course analyses, and investigations into chronic disease development. Our novel g-computation estimators provide a simultaneous solution.
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