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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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
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ISSN0361-0926
1532-415X
DOI10.1080/03610926.2021.1887241

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Abstract 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.
AbstractList 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.
Author Shen, Pao-sheng
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Cites_doi 10.1111/sjos.12113
10.1007/978-1-4612-5098-2
10.1111/j.1467-9469.2005.00457.x
10.1007/978-1-4757-2545-2
10.1080/03610926.2017.1390135
10.1093/biomet/91.2.277
10.1080/03610926.2019.1662046
10.1111/j.0006-341x.2003.00119.x
10.1080/03610926.2014.930908
10.1080/01621459.1989.10478873
10.1214/aos/1176347506
10.1214/aos/1176349140
10.1016/j.jmva.2010.01.010
10.1080/03610918.2019.1583343
10.1214/15-AOS1406
10.1111/sjos.12319
10.1214/aos/1176324462
10.1214/aos/1032181177
10.1093/biomet/89.3.659
10.1214/aos/1030741089
10.1090/conm/080/999011
10.1002/sim.4780080803
10.1016/j.spl.2004.10.014
10.1038/242247a0
10.1080/02331888.2019.1633327
10.1080/03610918.2012.721910
10.1093/biomet/asw013
10.1080/02331888.2018.1510933
10.1080/01621459.1991.10475010
10.1080/01621459.1974.10480146
10.1214/aos/1176350608
10.1111/1467-9469.00065
10.1002/sim.3018
10.1093/biomet/52.3-4.650
10.1016/j.csda.2012.06.001
10.1214/aos/1032298293
10.1111/j.2517-6161.1972.tb00899.x
10.1080/02664760903563635
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  doi: 10.1007/978-1-4612-5098-2
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  doi: 10.1111/j.1467-9469.2005.00457.x
– volume-title: Efficient and adaptive estimation for semiparametric models
  year: 1993
  ident: CIT0005
– ident: CIT0047
  doi: 10.1007/978-1-4757-2545-2
– ident: CIT0043
  doi: 10.1080/03610926.2017.1390135
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  doi: 10.1093/biomet/91.2.277
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  doi: 10.1080/03610926.2019.1662046
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  doi: 10.1111/j.0006-341x.2003.00119.x
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  doi: 10.1080/03610926.2014.930908
– ident: CIT0048
  doi: 10.1080/01621459.1989.10478873
– ident: CIT0008
  doi: 10.1214/aos/1176347506
– volume: 2
  start-page: 1
  volume-title: Lecture notes on mathematical statistics and probability
  year: 1980
  ident: CIT0001
– ident: CIT0017
  doi: 10.1214/aos/1176349140
– ident: CIT0020
  doi: 10.1016/j.jmva.2010.01.010
– ident: CIT0042
  doi: 10.1080/03610918.2019.1583343
– ident: CIT0045
  doi: 10.1214/15-AOS1406
– ident: CIT0024
  doi: 10.1111/sjos.12319
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  doi: 10.1214/aos/1176324462
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  doi: 10.1214/aos/1032181177
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  doi: 10.1093/biomet/89.3.659
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  doi: 10.1090/conm/080/999011
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  doi: 10.1002/sim.4780080803
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  doi: 10.1016/j.spl.2004.10.014
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  doi: 10.1038/242247a0
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  doi: 10.1080/02331888.2019.1633327
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  doi: 10.1080/03610918.2012.721910
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  doi: 10.1093/biomet/asw013
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  doi: 10.1080/02331888.2018.1510933
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  start-page: 59
  year: 2001
  ident: CIT0025
  publication-title: Scandinavian Journal of Statistics
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Snippet Double censored data often arise in medical and epidemiological studies when observations are subject to both left censoring and right censoring. In this...
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SubjectTerms additive hazard model
Algorithms
Asymptotic properties
Censored data (mathematics)
EM algorithm
Left censoring
Maximum likelihood estimation
Maximum likelihood estimators
MLE
Regression coefficients
Title The Cox-Aalen model for doubly censored data
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