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Biostatistician Community Webinar
November 14 @ 12:00 pm - 2:00 pm
Regression Models for Censored Data: When it’s NOT a good idea to use PH, AFT and other such models?
Presenter: Sujit Ghosh
Date and Time: Tuesday, November 14, 2017, 12:00 p.m. – 2:00 p.m. EST
Webinar location: Bloomberg School of Public Health, 3rd floor, Room E3609
In many clinical applications of survival analysis with covariates, majority of practitioners routinely choose to use proportional hazard (PH) based regression models when in fact it may not be appropriate and may even lead to erroneous inference. The commonly used semiparametric assumptions (e.g., AFT, PH and proportional odds, etc.) may turn out to be stringent and unrealistic, particularly when there is scientific background to believe that survival curves under different covariate combinations will cross during the study period. This webinar presents a very flexible class of nonparametric regression models for the conditional hazard function. The methodology presented is known to have three key features: (i) the smooth estimator of the conditional hazard rate has been shown to be a unique solution of a strictly convex optimization problem for a wide range of applications; making it computationally attractive, (ii) the model has been shown to encompass a proportional hazards structure, and (iii) large sample properties including consistency and convergence rates have been established under a set of mild regularity conditions. Following a brief introduction of the newly proposed methodology, the webinar will focus more on illustrating the empirical performances of the methods using several simulated and real case studies. The attendees are encouraged to read the published paper and related R codes will be provided at the webinar.