Hosted by the GPH Department of Biostatistics and NYU Grossman School of Medicine
Join NYU Grossman's Dr. Ivan Diaz for his presentation "Causal Survival Analysis Under Competing Risks Using Longitudinal Modified Treatment Policies."
Longitudinal modified treatment policies (LMTP) are a novel method to define and estimate causal parameters for a general class of exposures, including the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems to estimate total and mediated effects when the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as root-n-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.
About the Speaker:
Dr. Ivan Diaz is an Associate Professor in the Division of Biostatistics at the Department of Population Health of the NYU Grossman School of Medicine. His research focuses on the development of non-parametric statistical methods for causal inference from observational and randomized studies with complex datasets, using machine learning. This includes but is not limited to mediation analysis, methods for continuous exposures, longitudinal data including survival analysis, transportability of causal effects, and methodology for randomized trials. He is also interested in general semi-parametric theory, machine learning, and high-dimensional data.