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Efficient Estimation and Inference in the Proportional Odds Model for Survival Data

Author

Listed:
  • Xifen Huang

    (School of Mathematics, Yunnan Normal University, Kunming 650092, China)

  • Chaosong Xiong

    (School of Mathematics, Yunnan Normal University, Kunming 650092, China)

  • Tao Jiang

    (Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou 311508, China)

  • Junfeng Lu

    (Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou 311508, China)

  • Jinfeng Xu

    (Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou 311508, China)

Abstract

In modeling time-to-event data with long-term survivors, the proportional hazards model is widely used for its easy and direct interpretation as well as the flexibility to accommodate the past information and allow time-varying predictors. This becomes most relevant when the mortality of individuals converges with time, and the estimation and inference based upon the proportional odds model can often yield more accurate and reasonable results than the classical Cox’s proportional hazards model. Along with the fast development of the data science technologies, computational challenges for survival data with increasing sample size and diverging parameter dimension exist. Currently, existing methods for analyzing such data are computationally inconvenient. In this paper, we propose efficient computational methods for analyzing survival data in the proportional odds model, where the nonparametric maximum likelihood approach is combined with the minorization-maximization (MM) algorithm and the regularization scheme to yield fast and accurate estimation and inferential procedures. The illustration of the methodology using extensive simulation studies and then the application to the Veterans’ Administration lung cancer data is also given.

Suggested Citation

  • Xifen Huang & Chaosong Xiong & Tao Jiang & Junfeng Lu & Jinfeng Xu, 2022. "Efficient Estimation and Inference in the Proportional Odds Model for Survival Data," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3362-:d:916505
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    References listed on IDEAS

    as
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