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Reweighted estimators for additive hazard model with censoring indicators missing at random

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  • Xiaolin Chen

    (Qufu Normal University)

  • Jianwen Cai

    (University of North Carolina at Chapel Hill)

Abstract

Survival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without reweighting. The proposed methods are illustrated by analyzing a dataset from a breast cancer study.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:2:d:10.1007_s10985-017-9398-z
    DOI: 10.1007/s10985-017-9398-z
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    References listed on IDEAS

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    5. Xu, Qiang & Paik, Myunghee Cho & Luo, Xiaodong & Tsai, Wei-Yann, 2009. "Reweighting Estimators for Cox Regression With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1155-1167.
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    Cited by:

    1. Sheng, Ying & Wang, Qihua, 2020. "Conditional probability estimation based classification with class label missing at random," Journal of Multivariate Analysis, Elsevier, vol. 176(C).

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