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Diagnostic measures for the Cox regression model with missing covariates

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  • Hongtu Zhu
  • Joseph G. Ibrahim
  • Ming-Hui Chen

Abstract

We investigate diagnostic measures for assessing the influence of observations and model misspecification on the Cox regression model when there are missing covariate data. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is introduced to examine the effects of deleting individual observations on the estimates of finite- and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness-of-fit statistics for testing misspecification of the model assumptions. A resampling method is developed to approximate the $p$-values of the goodness-of-fit statistics. We conduct simulation studies to evaluate our methods, and analyse a real dataset to illustrate their use.

Suggested Citation

  • Hongtu Zhu & Joseph G. Ibrahim & Ming-Hui Chen, 2015. "Diagnostic measures for the Cox regression model with missing covariates," Biometrika, Biometrika Trust, vol. 102(4), pages 907-923.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:907-923.
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    References listed on IDEAS

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    1. Escanciano, J. Carlos, 2006. "A Consistent Diagnostic Test For Regression Models Using Projections," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1030-1051, December.
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