Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Charlotte Behning, Alexander Bigerl, Marvin N. Wright, Peggy Sekula, Moritz Berger, Matthias Schmid
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Abstract

Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor–response relationships and CIF estimates of renal events.

Abstract Image

具有竞争事件的随机生存森林:基于子分布的估算方法
随机生存森林(RSF)可应用于许多从时间到事件的研究问题,尤其适用于自变量与相关事件之间关系相当复杂的情况。然而,在许多临床环境中,相关事件的发生会受到竞争事件的影响,这意味着患者可能会经历除相关事件之外的其他结果。忽略竞争事件(即把竞争事件视为普查)通常会导致对累积发病率函数(CIF)的估计出现偏差。针对竞争事件的一种流行方法是 Fine 和 Gray 的子分布危险模型,该模型通过拟合定义在子分布时间尺度上的单一事件模型来直接估计 CIF。在此,我们将亚分布危害建模方法的概念整合到 RSF 中。我们开发了几种估算策略,在观测到竞争事件的情况下,使用离散时间子分布危害模型中的权重来估算删减时间。我们的模拟结果表明,如果在整个数据集上的森林外已经进行了估算,那么 CIF 就能得到很好的估计。特别是在相关事件发生率较低或剔除率较高的情况下,竞争事件不应被忽视,即应被视为剔除事件。在应用于真实世界的慢性肾病流行病学数据集时,估算方法得出了高度可信的预测因子-响应关系和肾病事件的 CIF 估计值。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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