5 papers · 1 filter
SPYCE: A Doubly Robust Estimator for Trials Targeting Early Huntington Disease under Outcome-Dependent Censoring
Kihyun Han, Yanyuan Ma, Karen Marder +1
Clinical trials for neurodegenerative diseases must identify sensitive endpoints -- outcomes that change rapidly enough to detect treatment effects. In Huntington disease, this req…
PRESCCO: Efficient Prediction Intervals under a Right-Censored Covariate
Kihyun Han, Yanyuan Ma, Karen Marder +1
In clinical studies, a patient's outcome, e.g., a cognitive test score, is typical or atypical depending on how it compares with the outcomes of patients at a similar point in a ne…
Robust Estimation under Outcome Dependent Right Censoring in Huntington Disease: Estimators for Low and High Censoring Rates
Jesus E. Vazquez, Yanyuan Ma, Karen Marder +1
Across health applications, researchers model outcomes as a function of time to an event, but the event time is right-censored for participants who exit the study or otherwise do n…
Robust and efficient estimation in the presence of a randomly censored covariate
Seong-ho Lee, Brian D. Richardson, Yanyuan Ma +2
In Huntington's disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, this entails modeling symptom severity as a func…
Establishing the Parallels and Differences Between Right-Censored and Missing Covariates
Jesus E. Vazquez, Marissa C. Ashner, Yanyuan Ma +2
While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as right-censored covariates, is now of growing interest. S…