Analysis of Semi-Competing Risks Data Using Copula-Based Models

Simulate and analyze Semi-competing Risks Data using copula-based models. The Semi-competing Risks Data consist of a terminal event time and single or multiple intermediate event times. The marginal survival functions of these event times are estimated without parametric assumptions. The association parameters measuring dependency among these event times involving the copula model are yielded from solving a concordance estimating equations or maximizing a pseudo-likelihood function. Details can be found in the article by Tonghui Yu and Liming Xiang (2026) .

CopulaSCR

Citation

Yu, Tonghui, and Xiang, Liming (2026).
Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis.
Biometrics, 82(2), ujag087.
https://doi.org/10.1093/biomtc/ujag087

Yu, Tonghui, Zhang, Binhui, Xiang, Liming, Ma, Jianwei, and Chen, Chixiang.
CopulaSCR: An R Package for the Analysis of Semi-competing Risks Data Using Copula-based Models.
Working paper.