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Activity Number: 86
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319477
Title: Shared Frailty in Joint Model of Cancer Incidence, Metastases, and Mortality
Author(s): Qui Tran* and Kelley M. Kidwell and Alex Tsodikov
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Semiparametric regression ; Survival analysis ; Disease natural history ; Marked endpoints ; Competing risks

In cancer studies, multiple events of interests such as diagnosis and metastasis can be observed in any order before the patient's death or end of the study. Sometimes, a particular event is unobserved and the exact time that it occurs is only known to be between other observed events, which leads to marked-endpoint type of data. In a previous paper, we proposed the use of a semi-parametric regression model to simultaneously model time to diagnosis, latent metastasis, and death. This paper proposes the use of a shared frailty term to model the latent correlation between the sequential events that is not accounted for by the shared baseline hazards and covariates. We derive the estimation procedure for the frailty term's parameters and other covariate effects via nonparametric maximum likelihood and Laplace transformation. This proposed model's estimation procedure is tested via Monte Carlo simulation and applied to the analysis of breast cancer data from SEER registry. The proposed model's ability to capture the correlation between time-of-diagnosis and time-of-death are demonstrated graphically via plots of post-diagnosis survivals conditioning on time-of-diagnosis.

Authors who are presenting talks have a * after their name.

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