This week's Biostatistics Seminar speaker, Dr. Sujit K. Ghosh of NCSU Statistics Department, will present, "Nonparametric regression models for right-censored data: Beyond proportional hazards".
In many clinical applications of survival analysis with covariates, the commonly used semiparametric assumptions (e.g., proportional hazards, proportional odds, AFT, 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 talk presents a nonparametric regression model for the conditional hazard rate using a suitable sieve of Bernstein polynomials. The proposed nonparametric methodology has three key features: (i) the smooth estimator of the conditional hazard rate is 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 is shown to encompass a proportional hazards structure, and (iii) large sample properties including consistency and convergence rates are established under a set of mild regularity conditions.
Empirical results based on several simulated data scenarios indicate that the proposed model has reasonably robust performance compared to other semiparametric models particularly when such semiparametric modeling assumptions are violated. The proposed method is further illustrated on the gastric cancer data and the Veterans Administration lung cancer data.
The work is based on a published article with Muhtar Osman (Amgen) available online: