activity
20152026
most citedHierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer

40 citations · 105 across the 14 of their papers we have counts for

collaborators

15 papers

stat.ME2026

Toward Joint Prediction of a Longitudinal Marker and a Terminal Event: A bivariate discrete-time framework

Stephanie Armbruster, Daniel Kramer, Rui Duan +3

Sudden cardiac death (SCD) is a leading cause of death in the U.S. Patients at elevated risk of SCD are primarily treated with an implantable cardioverter-defibrillator (ICD), whic…

stat.ME2024

Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data

Harrison T. Reeder, Sebastien Haneuse, Kyu Ha Lee

An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. Fo…

stat.ME2023★ 1 cited

Characterizing quantile-varying covariate effects under the accelerated failure time model

Harrison T. Reeder, Kyu Ha Lee, Sebastien Haneuse

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failu…

stat.ME2022★ 1 cited

Double sampling for informatively missing data in electronic health record-based comparative effectiveness research

Alexander W. Levis, Rajarshi Mukherjee, Rui Wang +2

Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, s…

stat.ME2022★ 16 cited

Penalized Estimation of Frailty-Based Illness-Death Models for Semi-Competing Risks

Harrison T. Reeder, Junwei Lu, Sebastien Haneuse

Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. S…

cs.LG2021★ 4 cited

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

Alexander Trott, Sunil Srinivasa, Douwe van der Wal +2

Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism…