TITLE:
Dependence Model Selection for Semi-Competing Risks Data
AUTHORS:
Jin-Jian Hsieh, Cheng-Fang Tsai
KEYWORDS:
Copula Model, Likelihood Function, Model Selection, Semi-Competing Risks Data
JOURNAL NAME:
Open Journal of Statistics,
Vol.10 No.2,
April
3,
2020
ABSTRACT: We consider the model selection problem of the dependency between theterminal 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 forsemi-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. Fromsimulation studies, it shows the performance of the proposed approach is well. Finally, we apply our method to a bone marrow transplant data set.