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Reduced rank proportional hazards model for competing risks: An application to a breast cancer trial

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Title: Reduced rank proportional hazards model for competing risks: An application to a breast cancer trial
Authors: Fiocco, M.1 m.fiocco@lumc.nl, Putter, H.1, van de Velde, C.J.H.2, van Houwelingen, J.C.1
Source: Journal of Statistical Planning & Inference. May2006, Vol. 136 Issue 5, p1655-1668. 14p.
Subjects: Cancer treatment, Competing risks, Regression analysis, Failure time data analysis
Abstract: Abstract: In many cancer trials patients are at risk of recurrence and death after the appearance and the successful treatment of the first diagnosed tumour. In this situation competing risks models that model several competing causes of therapy or surgery failure are a natural framework to describe the evolution of the disease. Typically, regression models for competing risks outcomes are based on proportional hazards model for each of the cause-specific hazard rates. An immediate practical problem is then how to deal with the abundance of regression parameters. The aim of reduced rank proportional hazards models is to reduce the number of parameters that need to be estimated while at the same time keeping the distinction between different transitions. They have the advantage of describing the competing risks model in fewer parameters, cope with transitions where few events are present and facilitate the interpretation of these estimates. We shall illustrate the use of this technique on 2795 patients from a breast cancer trial (EORTC 10854). [Copyright &y& Elsevier]
Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 19475210
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Reduced rank proportional hazards model for competing risks: An application to a breast cancer trial
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Statistical+Planning+%26+Inference%22">Journal of Statistical Planning & Inference</searchLink>. May2006, Vol. 136 Issue 5, p1655-1668. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Cancer+treatment%22">Cancer treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Competing+risks%22">Competing risks</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+time+data+analysis%22">Failure time data analysis</searchLink>
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  Label: Abstract
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  Data: Abstract: In many cancer trials patients are at risk of recurrence and death after the appearance and the successful treatment of the first diagnosed tumour. In this situation competing risks models that model several competing causes of therapy or surgery failure are a natural framework to describe the evolution of the disease. Typically, regression models for competing risks outcomes are based on proportional hazards model for each of the cause-specific hazard rates. An immediate practical problem is then how to deal with the abundance of regression parameters. The aim of reduced rank proportional hazards models is to reduce the number of parameters that need to be estimated while at the same time keeping the distinction between different transitions. They have the advantage of describing the competing risks model in fewer parameters, cope with transitions where few events are present and facilitate the interpretation of these estimates. We shall illustrate the use of this technique on 2795 patients from a breast cancer trial (EORTC 10854). [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Competing risks
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      – SubjectFull: Regression analysis
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      – SubjectFull: Failure time data analysis
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              Text: May2006
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