在竞争事件背景下，原因特异性累积发生率的反事实对比量化了治疗对关注事件的总体因果效应。然而，治疗对比赛项目的影响可能间接地促成了这一总效应，使其解释复杂化。我们之前提出了可分离效应（Stensrud et al，2019），以定义治疗对关注事件的直接和间接效应。该定义的前提是将治疗分解为两个成分，沿着两个独立的因果路径起作用，一个完全在竞争事件之外，另一个完全通过竞争事件。与以前的直接和间接效应定义不同，在对治疗成分进行单独干预的研究中，可以对可分离效应进行实证审查。在这里，我们扩展和推广的概念，可分离的影响，在几个方面，允许解释，识别和估计下相当弱的假设。我们提出并讨论了一个定义的可分离的效果，适用于一般的时变结构，其中的可分离的效果仍然可以有意义地解释，即使它们不能被视为直接和间接的影响。我们进一步推导出在尚未提供分解治疗的观察性研究中识别可分离效应的较弱条件;特别是，这些条件允许感兴趣事件、竞争事件和失访的常见原因随时间变化。对于这些一般设置，我们提出了半参数加权估计，是直接实现。作为一个例子，我们应用估计量来研究强化血压治疗对急性肾损伤的可分离影响，使用随机临床试验的数据。
英文摘要：
In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects (Stensrud et al, 2019) to define direct and indirect effects of the treatment on the event of interest. This definition presupposes a treatment decomposition into two components acting along two separate causal pathways, one exclusively outside of the competing event and the other exclusively through it. Unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in a study where separate interventions on the treatment components are available. Here we extend and generalize the notion of the separable effects in several ways, allowing for interpretation, identification and estimation under considerably weaker assumptions. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted, even when they cannot be regarded as direct and indirect effects. We further derive weaker conditions for identification of separable effects in observational studies where decomposed treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. For these general settings, we propose semi-parametric weighted estimators that are straightforward to implement. As an illustration, we apply the estimators to study the separable effects of intensive blood pressure therapy on acute kidney injury, using data from a randomized clinical trial.
Illustrating implications of misaligned causal questions and statistics in settings with competing events and interest in treatment mechanisms
Harvard Medical School（哈佛医学院）；Harvard Pilgrim Health Care Institute（哈佛皮尔格林健康保健研究所）；The University of Tokyo Hospital（东京大学医院）；Yale School of Public Health（耶鲁大学公共卫生学院）；Harvard T. H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）
Department of Statistics, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校统计学系）；Department of Human Development, Teachers College Columbia University（哥伦比亚大学教师学院人类发展系）
Nonparametric Inference with an Instrumental Variable under a Separable Binary Treatment Choice Model
Department of Statistics, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校统计学系）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计与数据科学系）
Estimation of separable direct and indirect effects in continuous time
许多研究问题涉及到时间事件的结果，可以防止发生由于竞争事件。在这些情况下，我们必须小心经典统计被估量的因果解释。特别是，估计的危害规模，如比率的原因具体或子分布的危害，是根本难以解释的因果关系。风险量表的估计值（如累积发生率函数的对比）确实具有因果解释，但它们仅捕获治疗对关注事件的总效应;即，通过竞争事件和竞争事件之外的效应。为了理清关注事件和竞争事件的因果处理效应，最近引入了可分离的直接和间接效应。在这里，我们提供了新的结果估计的直接和间接的可分离的影响在连续的时间。特别是，我们推导出连续时间的非参数影响函数，并用它来构造一个估计，具有一定的鲁棒性。我们还提出了一个简单的估计基于半参数模型的两个原因特定的风险函数。我们描述了这些估计量的渐近性质，并给出了模拟研究的结果，表明估计量在有限样本中表现令人满意。最后，我们重新分析了Stensrud et al（2020）的前列腺癌试验。
