Variable selection for recurrent event data via nonconcave penalized estimating function |
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Authors: | Xingwei Tong Liang Zhu Jianguo Sun |
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Institution: | (1) School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, People’s Republic of China;(2) Biostatistics, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105-3678, USA;(3) Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USA |
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Abstract: | Variable selection is an important issue in all regression analysis and in this paper, we discuss this in the context of regression
analysis of recurrent event data. Recurrent event data often occur in long-term studies in which individuals may experience
the events of interest more than once and their analysis has recently attracted a great deal of attention (Andersen et al.,
Statistical models based on counting processes, 1993; Cook and Lawless, Biometrics 52:1311–1323, 1996, The analysis of recurrent
event data, 2007; Cook et al., Biometrics 52:557–571, 1996; Lawless and Nadeau, Technometrics 37:158-168, 1995; Lin et al.,
J R Stat Soc B 69:711–730, 2000). However, it seems that there are no established approaches to the variable selection with
respect to recurrent event data. For the problem, we adopt the idea behind the nonconcave penalized likelihood approach proposed
in Fan and Li (J Am Stat Assoc 96:1348–1360, 2001) and develop a nonconcave penalized estimating function approach. The proposed
approach selects variables and estimates regression coefficients simultaneously and an algorithm is presented for this process.
We show that the proposed approach performs as well as the oracle procedure in that it yields the estimates as if the correct
submodel was known. Simulation studies are conducted for assessing the performance of the proposed approach and suggest that
it works well for practical situations. The proposed methodology is illustrated by using the data from a chronic granulomatous
disease study. |
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Keywords: | Estimating function Nonconcave penalized procedure Oracle procedure Recurrent event data Variable selection |
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