广义病例队列设计下多类型复发事件的加性转移模型

doi:10.16088/j.issn.1001-6600.2024060302

摘要/Abstract

摘要： 协变量信息采集成本昂贵是导致大型队列研究或随访型研究止步不前的主要原因,病例队列设计(case-cohort design)是解决这一问题的一种有偏抽样机制,在生存事件中已得到广泛研究。然而,多类型复发事件在生物医学和公共卫生研究中也极为常见,并且相关研究往往需要对试验对象进行长期跟踪,研究成本也可能较为高昂。鉴于此,本文基于一类加性转移模型提出多类型复发事件的广义病例队列设计,选择与时间相关的加权函数,应用逆概率加权方法建立未知参数的加权估计方程,并证明所得参数估计量的相合性和渐近正态性。通过数值模拟和实例分析验证所提方法的有效性。

关键词: 多类型复发事件, 广义病例队列设计, 加性转移模型, 逆概率加权

Abstract: The high cost of collecting covariate information is the main reason why large cohort studies or follow-up studies are halted, and Case-Cohort Design is a biased sampling mechanism to solve this problem, which has been extensively studied in survival events. However, multivariate recurrent events are also very common in biomedical and public health research, which often require long-term follow-up of trial subjects and can be costly. In view of this, this paper proposes a sampling scheme for the design of generalized case cohorts of multivariate recurrent events. By using a class of additive transfer models to fit the data, selecting the time-related weighting function, and useing the inverse probability weighting method, the unknown parameter estimation equation is established, which further proves the coincidence and asymptotic normality of the obtained parameter estimators. Finally, the effectiveness of the proposed method is verified by numerical simulation and case analysis.

Key words: multivariate recurrent events, generalized case cohort design, additive transformation model, inverse probability weighting

中图分类号: O212.1

田亮, 戴家佳, 李先琪. 广义病例队列设计下多类型复发事件的加性转移模型[J]. 广西师范大学学报（自然科学版）, 2025, 43(3): 113-127.

TIAN Liang, DAI Jiajia, LI Xianqi. Additive Transformation Model of Multivariate Recurrent Events Data under Generalized Case Cohort Design[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(3): 113-127.

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