5 papers
Debiased Machine Learning for Partially Linear Accelerated Failure Time Models
Tomoki Okuno, Sijie Zheng, Brendon Chau +3
The Cox model remains the default for survival analysis, but the proportional hazards assumption is often violated and hazard ratios can be difficult to interpret. Accelerated fail…
Two-Stage Least Squares Instrumental Variable Estimation for Semiparametric Accelerated Failure Time Models with Right-Censored Data
Zian Zhuang, Hua Zhou, Jin Zhou +1
Instrumental variable (IV) analysis is widely used in fields such as economics and epidemiology to address unobserved confounding and measurement error when estimating the causal e…
Efficient Implementation of a Semiparametric Joint Model for Multivariate Longitudinal Biomarkers and Competing Risks Time-to-Event Data
Shanpeng Li, Emily Ouyang, Jin Zhou +2
Joint modeling has become increasingly popular for characterizing the association between one or more longitudinal biomarkers and competing risks time-to-event outcomes. However, s…
Learn then Decide: A Learning Approach for Designing Data Marketplaces
Yingqi Gao, Wenlu Xu, Jin J. Zhou +3
As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive…
A Semiparametric Bayesian Method for Instrumental Variable Analysis with Partly Interval-Censored Time-to-Event Outcome
Elvis Han Cui, Xuyang Lu, Jin Zhou +2
This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confound…