High-Dimensional Genetic Survival Analysis with Kernel-Based Neural Networks

Oregon Convention Center 

Survival data analysis is pivotal in statistics and biostatistics, where the Cox Proportional Hazards (Cox PH) model stands out as a widely embraced approach. Recent technological and genetic advancements have broadened our understanding of disease-related genes, unveiling over 1800 identified disease-related genes. However, the complexity of identifying numerous genetic variants influencing disease progression arises from the intricate interplay between genetic and environmental factors, coupled with nonlinear and multifaceted relationships. To meet these challenges, we introduce a kernel-based neural network model. Similar to traditional neural networks, this model utilizes its hierarchical structure to learn complex features and interactions within genetic data. Simulations demonstrate that the kernel-based neural network model outperforms both the traditional Cox model and the Cox prediction model with PyTorch (PyCox) in terms of estimation and prediction accuracy, especially when handling nonlinear high-dimensional covariate effects. The advantages of our model over the Cox model and PyCox are further illustrated through real-world applications.

survival analysis

Cox proportional hazards model

kernel-based neural networks

high dimensional data

genetic analysis 

Section on Statistical Learning and Data Science