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On a simple estimation of the proportional odds model under right truncation

Author

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  • Peng Liu

    (Statistics and Actuarial Science, University of Kent)

  • Kwun Chuen Gary Chan

    (University of Washington)

  • Ying Qing Chen

    (Stanford Prevention Research Center)

Abstract

Retrospective sampling can be useful in epidemiological research for its convenience to explore an etiological association. One particular retrospective sampling is that disease outcomes of the time-to-event type are collected subject to right truncation, along with other covariates of interest. For regression analysis of the right-truncated time-to-event data, the so-called proportional reverse-time hazards model has been proposed, but the interpretation of its regression parameters tends to be cumbersome, which has greatly hampered its application in practice. In this paper, we instead consider the proportional odds model, an appealing alternative to the popular proportional hazards model. Under the proportional odds model, there is an embedded relationship between the reverse-time hazard function and the usual hazard function. Building on this relationship, we provide a simple procedure to estimate the regression parameters in the proportional odds model for the right truncated data. Weighted estimations are also studied.

Suggested Citation

  • Peng Liu & Kwun Chuen Gary Chan & Ying Qing Chen, 2023. "On a simple estimation of the proportional odds model under right truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 537-554, July.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:3:d:10.1007_s10985-022-09584-2
    DOI: 10.1007/s10985-022-09584-2
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    References listed on IDEAS

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    1. Lior Rennert & Sharon X. Xie, 2018. "Cox regression model with doubly truncated data," Biometrics, The International Biometric Society, vol. 74(2), pages 725-733, June.
    2. Strzalkowska-Kominiak, E. & Stute, W., 2009. "Martingale representations of the Lynden-Bell estimator with applications," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 814-820, March.
    3. Chiung-Yu Huang & Jing Qin, 2013. "Semiparametric estimation for the additive hazards model with left-truncated and right-censored data," Biometrika, Biometrika Trust, vol. 100(4), pages 877-888.
    4. Bella Vakulenko‐Lagun & Micha Mandel & Rebecca A. Betensky, 2020. "Inverse probability weighting methods for Cox regression with right‐truncated data," Biometrics, The International Biometric Society, vol. 76(2), pages 484-495, June.
    5. Y. Q. Chen & S. Cheng, 2005. "Semiparametric regression analysis of mean residual life with censored survival data," Biometrika, Biometrika Trust, vol. 92(1), pages 19-29, March.
    6. Ying Qing Chen & Nan Hu & Su-Chun Cheng & Philippa Musoke & Lue Ping Zhao, 2012. "Estimating Regression Parameters in an Extended Proportional Odds Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 318-330, March.
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