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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]
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Database: Engineering Source
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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]
ISSN:03783758
DOI:10.1016/j.jspi.2004.10.031