Jing Li, Ph.D. Student
Department of Biostatistics, IUPUI
Friday, March 8th, 2019, 1-2pm, HITS1110
Semi-competing risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a non-terminal event can be censored by a well-defined terminal event, but not vice versa. The shared gamma-frailty conditional Markov model (GFCMM) has been used to analyze semi-competing risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model does not always yield nonparametrically consistent estimation. In this talk, we provide a practical guideline for using the GFCMM in the analysis of semi-competing risk data that includes (i) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (ii) a graphical illustration to justify whether the unrestricted model yield nonparametrically consistent estimation for cases where the test provides a statistical significant result against the restricted model. This research was motivated by the Indianapolis Ibadan Dementia Project (IIDP) to explore whether dementia occurrence changes mortality risk.