Document Detail

Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure.
MedLine Citation:
PMID:  17661175     Owner:  NLM     Status:  MEDLINE    
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.
Xiaonan Xue; Mimi Y Kim; Roy E Shore
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Publication Detail:
Type:  Journal Article     Date:  2007-07-28
Journal Detail:
Title:  Lifetime data analysis     Volume:  13     ISSN:  1380-7870     ISO Abbreviation:  Lifetime Data Anal     Publication Date:  2007 Sep 
Date Detail:
Created Date:  2007-09-12     Completed Date:  2007-12-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9516348     Medline TA:  Lifetime Data Anal     Country:  United States    
Other Details:
Languages:  eng     Pagination:  333-50     Citation Subset:  IM    
Division of Biostatistics, Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA,
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MeSH Terms
Age Factors
Computer Simulation
Data Interpretation, Statistical*
Occupational Diseases / epidemiology*
Occupational Exposure
Proportional Hazards Models*
Radiation Injuries / epidemiology*
Survival Analysis*

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