Joint Statistical Seminar - Daniel Nevo

The Joint Statistical Seminars are aimed at researchers, employees, and students.

This week's seminar is given Associate Professor Daniel Nevo, Department of Statistics and Operations Research, Tel Aviv University 

Title: Causal inference for semi-competing risks data 

Abstract: An emerging challenge for time-to-event data is studying semi-competing risks, namely when two event times are of interest: a non-terminal event (e.g. Alzheimer's disease diagnosis) time, and a terminal event (e.g. death) time. The non-terminal event is observed only if it precedes the terminal event, which may occur before or after the non-terminal event. Studying treatment or intervention effects on the dual event times is complicated because for some units, the non-terminal event may occur under one treatment value but not under the other. Until recently, existing approaches generally disregarded the time-to-event nature of both outcomes. More recent research focused on principal strata effects within time-varying populations coupled with Bayesian estimation. In this talk, we will present alternative estimands, based on a single stratification of the population, corresponding to the scientific questions of interest. We present a novel assumption utilizing the time-to-event nature of the data, that is generally more flexible than the often-invoked monotonicity assumption. Our new assumption enables partial identifiability of causal effects of interest. We further present a frailty-based sensitivity analysis approach, and give conditions under which full identification is possible. We present non-parametric and semi-parametric estimation methods under right censoring. We illustrate the utility of our approach in a study of the causal effects of having APOE e4 allele on late-onset Alzheimer's disease and death.