发表机构
Sorbonne Université; Inserm; Sorbonne Public Health Institute; Equipe PEPITES; Assistance Publique - Hôpitaux de Paris; Hôpital Pitié Salpêtrière; Département de Santé Publique; Centre de Pharmacoépidémiologie (Cephepi); APHP; Hôpital Saint-Antoine; Inria; CNRS; Paris Brain Institute（索邦大学; 法国国家健康与医学研究院; 索邦公共卫生研究所; PEPITES团队; 巴黎公立协助医院; 皮蒂埃-萨尔佩特里耶尔医院; 公共卫生系; 药物流行病学中心; 巴黎公立协助医院; 圣安东医院; 法国国家计算机科学与控制研究院; 法国国家科学研究中心; 巴黎脑研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对目标试验模拟中的时间划分问题，提出间隔内因果排序并修正克隆-删失-加权估计器，以消除不朽时间偏倚和时变混杂，并指导合理选择时间间隔。
AI 中文摘要
在目标试验模拟中，患者遵循的治疗策略是根据他们在常规护理中实际接受的治疗推断出来的。然而，在大多数情况下，结局可能妨碍对计划治疗的观察，通过治疗策略的错误分类导致不朽时间偏倚，而治疗反应的标志物可能影响后续治疗决策，导致时变混杂。实现无偏治疗效果估计的关键步骤是将随访划分为足够短的时间间隔，以展开涉及治疗的反馈关系，并用有向无环图表示由此产生的因果关系。在本研究中，我们提出了由这种划分引起的间隔内因果排序，讨论了它们的因果含义，并评估了它们在临床环境中的合理性。对于每种因果排序，我们推导出相应的g公式。使用祖先多世界网络和模拟，我们表明当治疗在时间间隔内影响结局时，标准的克隆-删失-加权估计器无效，我们提出了一种改进版本的方法，在该设置中恢复其有效性。最后，我们分析了选择过宽或过窄时间间隔的后果，从而正式确立了时间划分的必要性，并提供了根据临床环境选择适当划分的实用指南。
英文摘要
In target trial emulation, the treatment strategies that patients follow are inferred from the treatments they actually receive in routine care. However, in most settings, the outcome may preclude the observation of planned treatment, giving rise to immortal time bias through misclassification of treatment strategy, while markers of treatment response may influence subsequent treatment decisions, giving rise to time-varying confounding. A key step toward unbiased treatment effect estimation is to partition follow-up into sufficiently short time intervals to unfold the feedback relationships involving treatment and represent the resulting causal relations with a directed acyclic graph. In this study, we present the possible within-interval causal orderings induced by this partitioning, discuss their causal implications, and assess their plausibility across clinical settings. For each causal ordering, we derive the corresponding g-formula. Using ancestral multi-world networks and simulations, we show that the standard cloning-censoring-weighting estimator is invalid when treatment affects the outcome within a time interval, and we propose a modified version of the method that restores its validity in this setting. Finally, we analyze the consequences of choosing time intervals that are either excessively wide or excessively narrow, thereby formally establishing the need for time partitioning and providing practical guidance for selecting an appropriate partition based on the clinical setting.
