How the mechanism of missing data on longitudinal biomarkers influences the survival analysis

DOI10.1080/03610926.2019.1622724OpenAlexW2948215654WikidataQ127707230 ScholiaQ127707230MaRDI QIDQ5078061

Feng-shou Ko

Publication date: 20 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2019.1622724

zbMATH Keywords

missing datarepeated measurementssurrogatebiomarkerheterogeneous baseline hazard

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Survival analysis. Techniques for censored and truncated data.
• A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error
• A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data
• A Joint Model for Survival and Longitudinal Data Measured with Error
• Joint modelling of longitudinal measurements and event time data
• Identification and efficacy of longitudinal markers for survival

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