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Pseudo-partial likelihood estimators for the Cox regression model with missing covariates

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  • Xiaodong Luo
  • Wei Yann Tsai
  • Qiang Xu

Abstract

By embedding the missing covariate data into a left-truncated and right-censored survival model, we propose a new class of weighted estimating functions for the Cox regression model with missing covariates. The resulting estimators, called the pseudo-partial likelihood estimators, are shown to be consistent and asymptotically normal. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when the observation probability is small and improve efficiency of estimating the missing covariate effects. Application to a practical example is reported. Copyright 2009, Oxford University Press.

Suggested Citation

  • Xiaodong Luo & Wei Yann Tsai & Qiang Xu, 2009. "Pseudo-partial likelihood estimators for the Cox regression model with missing covariates," Biometrika, Biometrika Trust, vol. 96(3), pages 617-633.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:3:p:617-633
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    File URL: http://hdl.handle.net/10.1093/biomet/asp027
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    Cited by:

    1. Shanshan Li & Yang Ning, 2015. "Estimation of covariate‐specific time‐dependent ROC curves in the presence of missing biomarkers," Biometrics, The International Biometric Society, vol. 71(3), pages 666-676, September.
    2. Na Hu & Xuerong Chen & Jianguo Sun, 2015. "Regression Analysis of Length-biased and Right-censored Failure Time Data with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 438-452, June.
    3. Xiaolin Chen & Jianwen Cai, 2018. "Reweighted estimators for additive hazard model with censoring indicators missing at random," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 224-249, April.
    4. Jon Arni Steingrimsson & Robert L. Strawderman, 2017. "Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1221-1235, July.
    5. Ertefaie Ashkan & Asgharian Masoud & Stephens David A., 2015. "Double Bias: Estimation of Causal Effects from Length-Biased Samples in the Presence of Confounding," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 69-89, May.
    6. Du, Mingyue & Li, Huiqiong & Sun, Jianguo, 2021. "Regression analysis of censored data with nonignorable missing covariates and application to Alzheimer Disease," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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