Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (3): 113-127.doi: 10.16088/j.issn.1001-6600.2024060302

• Mathematics and Statistics • Previous Articles     Next Articles

Additive Transformation Model of Multivariate Recurrent Events Data under Generalized Case Cohort Design

TIAN Liang, DAI Jiajia*, LI Xianqi   

  1. School of Mathematics and Statistics, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2024-06-03 Revised:2024-07-18 Online:2025-05-05 Published:2025-05-14

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

CLC Number:  O212.1
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