Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events
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时变行动下处理复发事件与半竞争事件的新型 g 计算算法
- 研究问题
- 在长期随访中，死亡会阻止非终末事件发生，干预与混杂因素又随时间变化，如何评估持续干预的因果影响？
- 方法与思路
- 作者提出两种处理时变行动与半竞争事件的 g 计算算法，通过蒙特卡洛模拟及吸烟预防与高血压的队列应用评估表现。
- 材料陈述的贡献
- 摘要称，两种估计量在所设模拟中偏差较小、区间覆盖适当；应用估计提示持续预防吸烟可小幅降低中年高血压患病率和死亡风险。
- 阅读关注点
- 应核对两种算法的目标参数、死亡与复发事件的处理方式、时变混杂的识别要求，以及模拟比较和置信区间的构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未说明完整识别条件、算法细节或效应数值，模拟优势不能直接推广。
原文摘要
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.