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Statistical inference under imputation for proportional hazard model with missing covariates

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  • Zhiping Qiu

Abstract

Missing covariate data are common in biomedical studies. In this article, by using the non parametric kernel regression technique, a new imputation approach is developed for the Cox-proportional hazard regression model with missing covariates. This method achieves the same efficiency as the fully augmented weighted estimators (Qi et al. 2005. Journal of the American Statistical Association, 100:1250) and has a simpler form. The asymptotic properties of the proposed estimator are derived and analyzed. The comparisons between the proposed imputation method and several other existing methods are conducted via a number of simulation studies and a mouse leukemia data.

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  • Zhiping Qiu, 2017. "Statistical inference under imputation for proportional hazard model with missing covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11575-11590, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:23:p:11575-11590
    DOI: 10.1080/03610926.2016.1275696
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