B1763
Title: Nonparametric maximum likelihood estimation of acceleratedfailure time models for competing risks
Authors: Sangbum Choi - Korea University (Korea, South) [presenting]
Abstract: Competing risks are common in clinical cancer research, as patients are subject to multiple potential failure outcomes, such as death from the cancer itself or from complications arising from the disease. In the analysis of competing risks, several regression methods are available for the evaluation of the relationship between covariates and cause-specific failures, many of which are based on Cox's proportional hazards model. Although a great deal of research has been conducted on estimating competing risks, less attention has been devoted to linear regression modeling, which is often referred to as the accelerated failure time (AFT) model in survival literature. We propose maximum likelihood inference procedures based on the kernel-smoothing principle for the AFT model under competing risks scenarios. The estimator is shown to be consistent and asymptotically normal. The performance of the new inference procedures is assessed through simulation studies, where the proposed estimator and the estimator from a cause-specific model are also compared. Illustrations with data from non-Hodgkin lymphoma patients are provided.
