Testing for Covariate Effect in the Cox Proportional Hazards Regression Model |
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Authors: | Karthik Devarajan Nader Ebrahimi |
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Institution: | 1. Division of Population Science , Fox Chase Cancer Center , Philadelphia , Pennsylvania , USA karthik.devarajan@fccc.edu;3. Division of Statistics , Northern Illinois University , DeKalb , Illinois , USA |
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Abstract: | This article presents methods for testing covariate effect in the Cox proportional hazards model based on Kullback–Leibler divergence and Renyi's information measure. Renyi's measure is referred to as the information divergence of order γ (γ ≠ 1) between two distributions. In the limiting case γ → 1, Renyi's measure becomes Kullback–Leibler divergence. In our case, the distributions correspond to the baseline and one possibly due to a covariate effect. Our proposed statistics are simple transformations of the parameter vector in the Cox proportional hazards model, and are compared with the Wald, likelihood ratio and score tests that are widely used in practice. Finally, the methods are illustrated using two real-life data sets. |
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Keywords: | Censored data Covariate effect Kullback–Leibler divergence Likelihood ratio Partial likelihood Proportional hazards Renyi's divergence Score Wald test |
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