Causal Inference in Survival Analysis: Emerging Methods for Complex Data Challenges

Wed, Aug 5, 2:00 PM - 3:50 PM Room CC-206B Thomas M. Menino Convention & Exhibition Center

Xiang MengOrganizerLarry HanChair

Biometrics Section co: Health Policy Statistics Sectionco: Section on Statistics in Epidemiology Applied

5 Presentations

A class of nonparametric methods for evaluating the effect of continuous treatments on survival outcomes

2:05 PM - 2:25 PM

Aaron Hudson (Fred Hutchinson Cancer Center)

Survival Analysis Nonparametric Statistics Causal Incidence Immune Correlates HIV COVID-19

Efficient and Debiased Learning of Average Hazard Under Non-Proportional Hazards

2:25 PM - 2:45 PM

Xiang Meng (Harvard University)

Causal Inference Efficient influence function Non-proportional hazards Average hazard Semiparametric inference Survival analysis

History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes

2:45 PM - 3:05 PM

Yuyao Wang (Brown University)

Co-authors: Alexander Levis (University of Pennsylvania), Shu Yang (North Carolina State University, Department of Statistics), Larry Han (Northeastern University)

conformal prediction survivor-conditional coverage time-varying covariates survival analysis time-to-event outcome

Leveraging machine learning to estimate survival curves with current status data

3:05 PM - 3:25 PM

Charles Wolock (University of Rochester, Department of Biostatistics and Computational Biology)

Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials

3:25 PM - 3:45 PM

Yi Liu

AMP HIV-1 prevention trials Time-to-event outcome Distribution shift Semiparametric efficiency theory Federated learning