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Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial

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  • Amy H. Herring
  • Joseph G. Ibrahim
  • Stuart R. Lipsitz

Abstract

Summary. Non‐ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality‐of‐life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality‐of‐life data are likely to be non‐ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non‐ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.

Suggested Citation

  • Amy H. Herring & Joseph G. Ibrahim & Stuart R. Lipsitz, 2004. "Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 293-310, April.
  • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:293-310
    DOI: 10.1046/j.1467-9876.2003.05168.x
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    Cited by:

    1. Soyoung Kim & Jae-Kwang Kim & Kwang Woo Ahn, 2022. "A calibrated Bayesian method for the stratified proportional hazards model with missing covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 169-193, April.
    2. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    3. Chen, Ming-Hui & Ibrahim, Joseph G. & Shao, Qi-Man, 2009. "Maximum likelihood inference for the Cox regression model with applications to missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2018-2030, October.
    4. Francesco Lagona & Zhen Zhang, 2008. "A missing composite covariate in survival analysis: a case study of the Chinese Longitudinal Health and Longevity Survey," MPIDR Working Papers WP-2008-022, Max Planck Institute for Demographic Research, Rostock, Germany.

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