Handling missingness value on jointly measured time-course and time-to-event data

DOI10.1080/03610918.2020.1851711arXiv2101.02580OpenAlexW3112803942MaRDI QIDQ6171283

Souvik Banerjee, Atanu Bhattacharjee, Gajendra K. Vishwakarma

Publication date: 17 July 2023

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.02580

zbMATH Keywords

missing datamultiple imputationjoint modelinglongitudinal responsesurvival outcome

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Missing data methods in longitudinal studies: a review
• Bayesian state-space modeling in gene expression data analysis: an application with biomarker prediction
• Recursive partitioning for missing data imputation in the presence of interaction effects
• Semiparametric theory and missing data.
• A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error
• Joint Models for Longitudinal and Time-to-Event Data
• Inference and missing data
• A Joint Model for Survival and Longitudinal Data Measured with Error
• Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Time-course data prediction for repeatedly measured gene expression
• Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates
• Modeling Longitudinal Data
• Joint modelling of longitudinal measurements and event time data

This page was built for publication: Handling missingness value on jointly measured time-course and time-to-event data