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Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure

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Abstract

This paper considers the analysis of time to event data in the presence of collinearity between covariates. In linear and logistic regression models, the ridge regression estimator has been applied as an alternative to the maximum likelihood estimator in the presence of collinearity. The advantage of the ridge regression estimator over the usual maximum likelihood estimator is that the former often has a smaller total mean square error and is thus more precise. In this paper, we generalized this approach for addressing collinearity to the Cox proportional hazards model. Simulation studies were conducted to evaluate the performance of the ridge regression estimator. Our approach was motivated by an occupational radiation study conducted at Oak Ridge National Laboratory to evaluate health risks associated with occupational radiation exposure in which the exposure tends to be correlated with possible confounders such as years of exposure and attained age. We applied the proposed methods to this study to evaluate the association of radiation exposure with all-cause mortality.

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Authors and Affiliations

  1. Division of Biostatistics, Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Belfer 1303C, Bronx, 10461, NY, USA

    Xiaonan Xue & Mimi Y. Kim

  2. Radiation Effects Research Foundation, 5-2 Hijiyama Koen, Minami-ku, Hiroshima, 732-0815, Japan

    Roy E. Shore

Authors
  1. Xiaonan Xue
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  2. Mimi Y. Kim
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  3. Roy E. Shore
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Correspondence to Xiaonan Xue.

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Xue, X., Kim, M.Y. & Shore, R.E. Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure. Lifetime Data Anal 13, 333–350 (2007). https://doi.org/10.1007/s10985-007-9045-1

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  • DOI: https://doi.org/10.1007/s10985-007-9045-1

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