ABSTRACT

Survival analysis or time-to-event studies focus on modeling the time of a future event, such as death or failure, and investigate its relationship with covariates or predictors of interest. Specifically, we may be interested in the causal effect of a given intervention or treatment on survival time. A typical question may be: will a given therapy increase the chances of survival of an individual or population? Such causal inquiries on survival outcomes are common in the fields of epidemiology and medicine. In this talk, I will introduce our recently proposed counterfactual inference framework for survival analysis which adjusts for bias from two sources, namely, confounding (from covariates influencing both the treatment assignment and the outcome) and censoring (informative or non-informative). To account for censoring biases, I will argue for flexible, nonparametric, and probabilistic neural time-to-event models that account for calibration and uncertainty, while predicting accurate absolute event times. Moreover, I will formulate a model-free nonparametric hazard ratio metric for comparing treatment effects or leveraging prior randomized real-world experiments in longitudinal studies. Further, I will use the proposed model-free hazard-ratio estimator to identify or stratify heterogeneous treatment effects. For stratifying risk profiles, I will also formulate an interpretable time-to-event driven clustering method of patients via a Bayesian nonparametric stick-breaking representation of the Dirichlet Process. Finally, I will present extensive results on challenging datasets, such as the Framingham Heart Study and the AIDS clinical trials group (ACTG).

BIOGRAPHY

Paidamoyo Chapfuwa is a postdoctoral research fellow mentored by Dr. Sherri Rose at the Stanford University Department of Health Policy. She received B.S.E. with distinction, M.S., and Ph.D. (advised by Drs. Lawrence Carin and Ricardo Henao) degrees in electrical and computer engineering from Duke University. Her extensive industry experience includes software engineering at Thoughtworks and research internships at Microsoft Research. Paidamoyo's research focuses on developing causal survival analysis methods to enable individualized decision-making from clinical data such as electronic health records and more recently, biological datasets (e.g., immunomics and synthetic biology). Her work incorporates statistical techniques from causal inference, generative modeling, and Bayesian nonparametrics. Paidamoyo's work has culminated in publications at top machine learning venues such as IEEE, ACM, ACL, and ICML. See https://paidamoyo.github.io for more information.