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.
Highlights
Semi-competing risks data [1] were often encountered in a biomedical study in which a terminal event censors a non-terminal event
The data can be divided into three different groups, acute lymphoblastic leukemia (ALL) with 38 patients, acute myelocytic leukemia (AML) low-risk with 54 patients, and AML high-risk with 45 patients
We study the model selection problem under semi-competing risks data
Summary
Semi-competing risks data [1] were often encountered in a biomedical study in which a terminal event censors a non-terminal event. An association model for semi-competing risks data is necessary for copula-based approaches [1]-[6], and it is important to select an appropriate dependence model for a data set. Tsai the likelihood function under several candidate models for the semi-competing risks data and use the likelihood function to select a most fitted model.
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