다음주 수요일 (12/17) 오전 11:00-12:00에 대우관 본관 535호에서 

통계연구소 주최로 다음의 세미나가 열릴 예정입니다. 

대학원생들의 많은 참여 부탁드립니다. 

- SPEAKER : Sangbum Choi, Assistant Professor, Department of Internal Medicine, University of Texas Health Science Center at Houston. 

- TITLE : Efficient semiparametric mixture inferences on cure rate models for competing risks. 

- ABSTRACT : 

Cancer patients may die from causes other than the diagnosed cancer. In a study of patients treated for soft tissue sarcoma, the patients may die from the disease or die without experiencing disease recurrence. In addition, a substantial proportion of the patients will remain cancer-free after surgical resection of the tumor, and therefore will not be at increased risk of any type of failure. Our goal  is to describe the effect of adjuvant chemotherapy simultaneously on the probabilities of long-term survival, death from cancer, or death from other causes. To this end, we propose a semiparametric mixture model to determine the effects of factors on the probability of occurrence, allowing the surviving fraction, and the hazard rate conditional on each of the failure types. These quantities are combined in the mixture approach using a multinomial logistic model and a class of semiparametric transformation models. Estimation of the regression and nonparametric parameters is achieved with a novel nonparametric maximum likelihood approach. Statistical inferences can be conveniently made from the inverse of the observed information matrix. Simulation studies show that the procedures work well in practical settings. The methodology is illustrated with data from the soft tissue sarcoma study.