5 citations · 5 across the 5 of their papers we have counts for
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…
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 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,…
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Shanpeng Li, Ning Li, Hong Wang +3
Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This pa…
A Flexible Joint Model for Multiple Longitudinal Biomarkers and A Time-to-Event Outcome: With Applications to Dynamic Prediction Using Highly Correlated Biomarkers
Ning Li, Yi Liu, Shanpeng Li +2
In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typi…