Dependence Model Selection for Semi-Competing Risks Data

Abstract

We consider the model selection problem of the dependency between the terminal event and the non-terminal event under semi-competing risks data. When the relationship between the two events is unspecified, the inference on the non-terminal event is not identifiable. We cannot make inference on the non-terminal event without extra assumptions. Thus, an association model for semi-competing risks data is necessary, and it is important to select an appropriate dependence model for a data set. We construct the likelihood function for semi-competing risks data to select an appropriate dependence model. From simulation studies, it shows the performance of the proposed approach is well. Finally, we apply our method to a bone marrow transplant data set. KeywordsCopula Model, Likelihood Function, Model Selection, Semi-Competing Risks Data et al. [4] used a distance measure between a non-parametric estimator and a model based estimator to select a proper dependence model. In this paper, we construct

Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly

“…Several copula selection methods are developed for semi-competing risks data under right censoring. 45,46 However, there is a lack of goodness-of-fit tests under interval censoring. In CLHLS, we use AIC to guide the model selection for simplicity.…”