Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm
Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Scheike, Thomas H. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Lifetime data analysis - Springer US, 1995, 13(2007), 3 vom: 08. Aug., Seite 399-420 |
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| Übergeordnetes Werk: |
volume:13 ; year:2007 ; number:3 ; day:08 ; month:08 ; pages:399-420 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10985-007-9043-3 |
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OLC2067134787 |
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| 520 | |a Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. | ||
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10.1007/s10985-007-9043-3 doi (DE-627)OLC2067134787 (DE-He213)s10985-007-9043-3-p DE-627 ger DE-627 rakwb eng 510 004 VZ Scheike, Thomas H. verfasserin aut Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. Cox regression model Tied survival data EM-algorithm Asymptotics Sun, Yanqing aut Enthalten in Lifetime data analysis Springer US, 1995 13(2007), 3 vom: 08. Aug., Seite 399-420 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:13 year:2007 number:3 day:08 month:08 pages:399-420 https://doi.org/10.1007/s10985-007-9043-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 13 2007 3 08 08 399-420 |
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10.1007/s10985-007-9043-3 doi (DE-627)OLC2067134787 (DE-He213)s10985-007-9043-3-p DE-627 ger DE-627 rakwb eng 510 004 VZ Scheike, Thomas H. verfasserin aut Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. Cox regression model Tied survival data EM-algorithm Asymptotics Sun, Yanqing aut Enthalten in Lifetime data analysis Springer US, 1995 13(2007), 3 vom: 08. Aug., Seite 399-420 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:13 year:2007 number:3 day:08 month:08 pages:399-420 https://doi.org/10.1007/s10985-007-9043-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 13 2007 3 08 08 399-420 |
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10.1007/s10985-007-9043-3 doi (DE-627)OLC2067134787 (DE-He213)s10985-007-9043-3-p DE-627 ger DE-627 rakwb eng 510 004 VZ Scheike, Thomas H. verfasserin aut Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. Cox regression model Tied survival data EM-algorithm Asymptotics Sun, Yanqing aut Enthalten in Lifetime data analysis Springer US, 1995 13(2007), 3 vom: 08. Aug., Seite 399-420 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:13 year:2007 number:3 day:08 month:08 pages:399-420 https://doi.org/10.1007/s10985-007-9043-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 13 2007 3 08 08 399-420 |
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10.1007/s10985-007-9043-3 doi (DE-627)OLC2067134787 (DE-He213)s10985-007-9043-3-p DE-627 ger DE-627 rakwb eng 510 004 VZ Scheike, Thomas H. verfasserin aut Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. Cox regression model Tied survival data EM-algorithm Asymptotics Sun, Yanqing aut Enthalten in Lifetime data analysis Springer US, 1995 13(2007), 3 vom: 08. Aug., Seite 399-420 (DE-627)233193332 (DE-600)1393066-7 (DE-576)07005777X 1380-7870 nnns volume:13 year:2007 number:3 day:08 month:08 pages:399-420 https://doi.org/10.1007/s10985-007-9043-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_4305 AR 13 2007 3 08 08 399-420 |
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Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. © Springer Science+Business Media, LLC 2007 |
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Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. © Springer Science+Business Media, LLC 2007 |
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Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. A simulation study is conducted to compare the finite sample properties of the methods. © Springer Science+Business Media, LLC 2007 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2067134787</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503171427.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2007 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10985-007-9043-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2067134787</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10985-007-9043-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Scheike, Thomas H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2007</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We consider tied survival data based on Cox proportional regression model. The standard approaches are the Breslow and Efron approximations and various so called exact methods. All these methods lead to biased estimates when the true underlying model is in fact a Cox model. In this paper we review the methods and suggest a new method based on the missing-data principle using EM-algorithm that leads to a score equation that can be solved directly. This score has mean zero. We also show that all the considered methods have the same asymptotic properties and that there is no loss of asymptotic efficiency when the tie sizes are bounded or even converge to infinity at a given rate. 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