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Cumulative risk regression in case-cohort studies using pseudo-observations.
Lifetime Data Analysis ( IF 1.3 ) Pub Date : 2020-01-13 , DOI: 10.1007/s10985-020-09492-3
Erik T Parner 1 , Per K Andersen 2 , Morten Overgaard 1
Affiliation  

Case–cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case–cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case–cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case–cohort survival data. We adjust for the case–cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

中文翻译:

使用伪观察的病例队列研究中的累积风险回归。

当有关某些风险因素的信息难以确定或成本高昂时,病例队列研究很有用。特别是,病例队列研究可能非常适合于对多个病例系列感兴趣的情况,例如在具有竞争风险的研究中,因为相同的子队列可以作为所有病例系列的比较组。以前对此类抽样队列数据的分析最常涉及基于 Cox 回归模型的比率估计。但是,对于竞争风险,此方法将不提供直接描述协变量与累积风险之间关联的参数。在本文中,我们使用伪观察研究了病例队列研究中特定原因累积风险的回归分析。我们主要关注具有竞争风险的情况。然而,作为副产品,我们还开发了一种方法,可以直接从病例队列生存数据中分析绝对死亡风险。我们通过应用于广义估计方程的逆采样概率来调整案例队列采样。在模拟研究中开发了所提出的估计器的大样本特性并评估了小样本特性。我们应用该方法来研究特定饮食成分和特定基因对心房颤动绝对风险的影响。
更新日期:2020-01-13
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