5 papers
FastJM: An R Package for Efficient Implementation of Semiparametric Joint Models for Longitudinal and Survival Data
Shanpeng Li, Emily Ouyang, Ace Isabel Mejia-Sanchez +2
Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. H…
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…
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…
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…
A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome
Shanpeng Li, Daniel S. Nuyujukian, Robyn L. McClelland +4
Motivated by a growing body of research emphasizing the importance of modeling within-subject (WS) variability in longitudinal biomarkers and its association with health outcomes,…