Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
The Annals of Statistics , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
citing papers explorer
-
Semi-supervised Method for Risk Prediction with Doubly Censored EHR Data
Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
-
Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.