| Speaker: | Zhangsheng Yu, Shanghai Jiao Tong University |
| Host: | Bo Fu, School of Data Science, Fudan University |
| Time: | 15:00-16:00, November 21, 2019 |
| Location: | Zibin N102, Fudan University |
| Abstract: | Survival data including potentially cured subjects are common in clinical studies and mixture cure rate models are often used for analysis. The non-cured probabilities are often predicted by non-parametric, high-dimensional, or even unstructured (e.g. image) predictors, which is a challenging for traditional nonparametric methods such as spline and local kernel. We propose to use the neural network to model the nonparametric or unstructured predictors' effect in cure rate models and retain the proportional hazards structure due to its explanatory ability. We estimate the parameters by Expectation-Maximization (EM) algorithm. Estimators are showed to be consistent. Simulation studies show good performance in both prediction and estimation. Finally, we analyze Primary Biliary Cirrhosis (PBC) data and Open Access Series of Imaging Studies (OASIS) data to illustrate the practical use of our methods. |
| Bio: | Dr. Zhangsheng Yu is a professor in Department of Bioinformatics and Biostatistics, Department of Statistics at Shanghai Jiao Tong University, Associate Director, SJTU-Yale Joint Center for Biostatistics and Data Sciences. He is also a professor adjunct at Department of Biostatistics, Yale University. His methodology researches include survival analysis, nonparametric regression, neural network, and clinical trials. He also collaborates extensively with investigators in biomedical areas including hepatology and nephrology. |