Competing risks regression with dependent multiple spells: Monte Carlo evidence and an application to maternity leave

Copulas are a convenient tool for modelling dependencies in competing risks models with multiple spells. This paper introduces several practical extensions to the nested copula model and focuses on the choice of the hazard model and copula. A simulation study looks at the relevance of the assumed parametric or semiparametric model for hazard functions, copula and whether a full or partial maximum likelihood approach is chosen. The results show that the researcher must be careful which hazard is being specifed as similar functional form assumptions for the subdistribution and cause-specifc hazard will lead to diferences in estimated cumulative incidences. Model selection tests for the choice of the hazard model and copula are found to provide some guidance for setting up the model. The nice practical properties and fexibility of the copula model are demonstrated with an application to a large set of maternity leave periods of mothers for up to three maternity leave periods.

Lipowski, Cäcilia, Simon M. S. Lo, Shuolin Shi and Ralf Wilke (2021), Competing risks regression with dependent multiple spells: Monte Carlo evidence and an application to maternity leave, Japanese Journal of Statistics and Data Science