具有重复事件和竞争事件的因果推理。

IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Lifetime Data Analysis Pub Date : 2024-01-01 Epub Date: 2023-05-12 DOI:10.1007/s10985-023-09594-8
Matias Janvin, Jessica G Young, Pål C Ryalen, Mats J Stensrud
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引用次数: 3

摘要

许多研究问题都涉及对同一人可能多次出现的结果的治疗效果。例如,医学研究人员对心力衰竭患者住院治疗和运动员运动损伤的治疗效果很感兴趣。死亡等竞争事件会使复发性事件研究中的因果推断复杂化,因为一旦发生竞争事件,个体就不可能再发生更多的复发性事件。在有竞争事件和没有竞争事件的情况下,对一些重复事件中的统计估计值进行了研究。然而,这些估计值的因果解释,以及从观测数据中识别这些估计值所需的条件,都还没有正式确定下来。在这里,我们使用一个因果推理的正式框架,在有竞争事件和无竞争事件的重复事件环境中提出几个因果估计值。当存在竞争事件时,我们将阐明常用的经典统计估计值何时可解释为因果中介文献中的因果量,如(受控)直接效应和总效应。此外,我们还展示了最近关于干预性中介估计值的研究成果,这使我们能够定义新的因果估计值,这些估计值具有重复性和竞争性事件,在许多主题设置中可能具有特殊的临床意义。我们使用因果有向无环图和单一世界干预图来说明如何根据主题知识推理各种因果估计值的识别条件。此外,我们利用计数过程的结果表明,我们在离散时间中阐述的因果估计值及其识别条件,在时间精细离散化的极限中收敛于经典的连续时间对应条件。我们提出了各种识别函数的估计值并确定了它们的一致性。最后,我们使用所提出的估计器,利用收缩压干预试验的数据计算了降压治疗对急性肾损伤复发的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Causal inference with recurrent and competing events.

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.

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来源期刊
Lifetime Data Analysis
Lifetime Data Analysis 数学-数学跨学科应用
CiteScore
2.30
自引率
7.70%
发文量
43
审稿时长
3 months
期刊介绍: The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
期刊最新文献
Conditional modeling of recurrent event data with terminal event. Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference. Optimal survival analyses with prevalent and incident patients. Two-stage pseudo maximum likelihood estimation of semiparametric copula-based regression models for semi-competing risks data. Nonparametric estimation of the cumulative incidence function for doubly-truncated and interval-censored competing risks data.
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