Web-Based Lectures

Title: Deep Learning Methods for Survival Analysis
Presenter: Ying Ding, Department of Biostatistics, University of Pittsburgh, PA, USA
Date and Time: November 28, 2023, 12:00 p.m. – 2:00 p.m. ET (This webinar will be taught via Zoom)
Sponsor: Lifetime Data Science Section

Registration Deadline: TBA

This webinar covers recent developments in deep learning-based methods for survival data analysis and provides case studies to apply these methods. Part 1 will introduce various neural networks for analyzing time-to-event data under different censoring mechanisms. Part 2 will introduce deep learning methods for estimating individualized/conditional average treatment effects for survival outcomes under the causal inference framework. In each part, we will demonstrate the implementation of the methods using R and Python and use case studies to illustrate the applications of these methods for biomedical and health research.

Part 1 – Deep Learning for Survival Analysis and Predictions

- Neural networks for right-censored survival data with time-independent or time-dependent covariates
- Case Study 1: Prediction of Progression of AMD (Age-related Macular Degeneration)
- Neural networks for interval-censored (and left truncated) survival data
- Case Study 2: Prediction of Development of AD (Alzheimer’s Disease)

Part 2 – Deep Learning for Causal Survival Analysis

- CATE (conditional average treatment effect) for survival outcomes
- Deep learning approaches for estimating CATE with survival outcomes
- Case Study 3: Childhood Asthma EHR data analysis

Registration Fees:
Lifetime Data Science Section Members: $20
ASA Members: $30
Student ASA Member: $25
Nonmembers: $45


Access Information

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