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AI 根据所提供的论文材料归类:摘要研究医学队列的半竞争风险与生存预测,不能因出现风险预测而归入金融风险等所列方向。

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关注这篇论文的作者
Malka Gorfine
Nir Keret
Asaf Ben Arie
David Zucker
Li Hsu

基于边际化脆弱性的疾病—死亡模型:英国生物样本库应用

研究问题
如何在疾病诊断与死亡相互依赖、入组存在延迟且部分早期死亡不可见时,利用健康队列构建可解释的疾病风险预测模型?
方法与思路
作者采用共享脆弱性刻画疾病与死亡时间的依赖,建立边际风险函数服从 Cox 模型的疾病—死亡模型,并设计相应估计程序。
材料陈述的贡献
摘要称估计程序可处理延迟入组、既往与新发病例信息合并,以及最低入组年龄之前死亡不可观测等队列数据挑战。
阅读关注点
应核对共享脆弱性与边际 Cox 模型如何兼容、延迟入组和早期死亡缺失的处理,以及风险预测的解释和识别基础。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;摘要未说明脆弱性分布、截断机制假设或风险预测的验证表现。

原文摘要

The UK Biobank is a large-scale health resource comprising genetic, environmental, and medical information on approximately 500,000 volunteer participants in the United Kingdom, recruited at ages 40–69 during the years 2006–2010. The project monitors the health and well-being of its participants. This work demonstrates how these data can be used to yield the building blocks for an interpretable risk-prediction model, in a semiparametric fashion, based on known genetic and environmental risk factors of various chronic diseases, such as colorectal cancer. An illness-death model is adopted, which inherently is a semi-competing risks model, since death can censor the disease, but not vice versa. Using a shared-frailty approach to account for the dependence between time to disease diagnosis and time to death, we provide a new illness-death model that assumes Cox models for the marginal hazard functions. The recruitment procedure used in this study introduces delayed entry to the data. An additional challenge arising from the recruitment procedure is that information coming from both prevalent and incident cases must be aggregated. Lastly, we do not observe any deaths prior to the minimal recruitment age, 40. In this work, we provide an estimation procedure for our new illness-death model that overcomes all the above challenges. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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