An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to ...

To assess the prognostic value of lymph node ratio (LNR) in patients with stage IV thyroid cancer based on the Surveillance, Epidemiology, and End Results (SEER) database. A total of 4,940 eligible patients were included for the analysis. Kaplan-Meier survival analysis and Cox proportional hazard regression were used to reveal the effect of LNR on overall survival (OS) and disease specific surv...

The objective of hazards (lifetime) analysis is to advance and promote statistical science in the various applied fields that deal with lifetime (survival) data including: actuarial science and reliability engineering. The lifetime data analysis provides special techniques that are required to compare the risks for failure. Bayesian semi-parametric methods have been applied to survival analysis...

s for Seminar Series available at: http://www.mcgill.ca/epi-biostat-occh/seminars/biostatistics/ Professor Michael Schemper Section for Clinical Biometrics, Core Unit for Medical Statistics and Informatics, Medical University of Vienna, Vienna, Austria. The estimation of average hazard ratios by Cox regression. Tuesday, 27 January, 2009 4:00 pm 5:00 pm Purvis Hall, 1020 Pine Ave. West, Room 24

Cox proportional hazards model is the most commonly used regression model to analyze survival data. The regression parameters in a Cox model can be estimated by maximizing the partial likelihood (Cox 1972, 1975). When the association is presented in the data due to matching design, recurrent events or multiple types of failure, the marginal approach can be used to adjust for association. In thi...