Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm |
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Authors: | Thomas H Scheike Yanqing Sun |
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Institution: | 1.Department of Biostatistics,University of Copenhagen,Copenhagen K,Denmark;2.Department of Mathematics and Statistics,The University of North Carolina at Charlotte,Charlotte,USA |
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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. |
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Keywords: | Cox regression model Tied survival data EM-algorithm Asymptotics |
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