The Cox-Aalen model for doubly censored data

Double censored data often arise in medical and epidemiological studies when observations are subject to both left censoring and right censoring. In this article, based on doubly censored data, we consider maximum likelihood estimation for the Cox-Aalen model with fixed covariates. By treating left...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 51; no. 23; pp. 8075 - 8092
Main Author Shen, Pao-sheng
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 06.10.2022
Taylor & Francis Ltd
Subjects
additive hazard model
Algorithms
Asymptotic properties
Censored data (mathematics)
EM algorithm
Left censoring
Maximum likelihood estimation
Maximum likelihood estimators
MLE
Regression coefficients
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Summary:Double censored data often arise in medical and epidemiological studies when observations are subject to both left censoring and right censoring. In this article, based on doubly censored data, we consider maximum likelihood estimation for the Cox-Aalen model with fixed covariates. By treating left censored observations as missing, we propose expectation-maximization (EM) algorithms for obtaining the maximum likelihood estimators (MLE) of the regression coefficients for the Cox-Aalen model. We establish the asymptotic properties of the MLE. Simulation studies show that MLE via the EM algorithms performs well.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2021.1887241