发表机构
Karolinska Institutet; Stockholm University（卡罗林斯卡学院; 斯德哥尔摩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种非参数估计量，将相对生存概念扩展至复发事件，结合队列与人群数据估计超额复发事件数，并证明其渐近性质及提供实际应用。
AI 中文摘要
衡量疾病负担是公共卫生研究和健康经济学的重要组成部分。一些疾病负担的度量，如住院次数，由复发事件构成。然而，评估哪些事件与特定疾病相关并非易事。我们通过开发一种新颖的非参数估计量，将相对生存的概念扩展到复发事件。该估计量结合了某队列的数据与汇总的人群水平数据，以估计超额复发事件的数量。利用经验过程理论，我们证明了在温和的正则条件下，该估计量弱收敛于一个均值为零的高斯过程，并提供了协方差函数的一致估计量。我们还通过模拟评估了该估计量的有限样本性质，并使用瑞典直肠癌患者的数据提供了一个实际示例。
英文摘要
Measuring disease burden is an important part of both public health research and health economics. Some measures of disease burden, such as hospitalisations, are made up of recurrent events. Assessing what events are related to a particular disease is however non-trivial. We extend the notion of relative survival to recurrent events by developing a novel non-parametric estimator. The estimator combines data from some cohort with aggregated population level data to estimate the number of excess recurrent events. Using empirical process theory, we show that the estimator converges weakly to a mean zero Gaussian process under mild regularity conditions, and provide a consistent estimator for the covariance function. We also evaluate the finite sample properties of the estimator through simulations and provide a practical example using data from Swedish patients with rectal cancer.
