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A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates

Author

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  • Ruiwen Zhou

    (University of Missouri)

  • Huiqiong Li

    (Yunnan University)

  • Jianguo Sun

    (University of Missouri)

  • Niansheng Tang

    (Yunnan University)

Abstract

This paper discusses the fitting of the proportional hazards model to interval-censored failure time data with missing covariates. Many authors have discussed the problem when complete covariate information is available or the missing is completely at random. In contrast to this, we will focus on the situation where the missing is at random. For the problem, a sieve maximum likelihood estimation approach is proposed with the use of I-spline functions to approximate the unknown cumulative baseline hazard function in the model. For the implementation of the proposed method, we develop an EM algorithm based on a two-stage data augmentation. Furthermore, we show that the proposed estimators of regression parameters are consistent and asymptotically normal. The proposed approach is then applied to a set of the data concerning Alzheimer Disease that motivated this study.

Suggested Citation

  • Ruiwen Zhou & Huiqiong Li & Jianguo Sun & Niansheng Tang, 2022. "A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 335-355, July.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:3:d:10.1007_s10985-022-09550-y
    DOI: 10.1007/s10985-022-09550-y
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    References listed on IDEAS

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    Cited by:

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    2. Hao Zeng & Xuxue Sun & Kuo Wang & Yuxin Wen & Wujun Si & Mingyang Li, 2024. "A Bayesian Approach for Lifetime Modeling and Prediction with Multi-Type Group-Shared Missing Covariates," Mathematics, MDPI, vol. 12(5), pages 1-23, February.

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