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Inverse probability weighted Cox regression for doubly truncated data

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  • Micha Mandel
  • Jacobo de Uña†à lvarez
  • David K. Simon
  • Rebecca A. Betensky

Abstract

Doubly truncated data arise when event times are observed only if they fall within subject†specific, possibly random, intervals. While non†parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease.

Suggested Citation

  • Micha Mandel & Jacobo de Uña†à lvarez & David K. Simon & Rebecca A. Betensky, 2018. "Inverse probability weighted Cox regression for doubly truncated data," Biometrics, The International Biometric Society, vol. 74(2), pages 481-487, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:481-487
    DOI: 10.1111/biom.12771
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    References listed on IDEAS

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    1. Moreira, C. & de Uña-Álvarez, J. & Meira-Machado, L., 2016. "Nonparametric regression with doubly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 294-307.
    2. Pao-sheng Shen, 2013. "Regression analysis of interval censored and doubly truncated data with linear transformation models," Computational Statistics, Springer, vol. 28(2), pages 581-596, April.
    3. Chiung-Yu Huang & Jing Qin & Dean A. Follmann, 2012. "A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling," Biometrika, Biometrika Trust, vol. 99(1), pages 199-210.
    4. Shen, Yu & Ning, Jing & Qin, Jing, 2009. "Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1192-1202.
    5. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
    6. Wei Yann Tsai, 2009. "Pseudo-partial likelihood for proportional hazards models with biased-sampling data," Biometrika, Biometrika Trust, vol. 96(3), pages 601-615.
    7. Pao-sheng Shen, 2010. "Nonparametric analysis of doubly truncated data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(5), pages 835-853, October.
    8. Shen, Pao-sheng, 2009. "Hazards regression for length-biased and right-censored data," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 457-465, February.
    9. Moreira, Carla & de Uña-Álvarez, Jacobo & Crujeiras, Rosa M., 2010. "DTDA: An R Package to Analyze Randomly Truncated Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i07).
    10. Jane Paik Kim & Wenbin Lu & Tony Sit & Zhiliang Ying, 2013. "A Unified Approach to Semiparametric Transformation Models Under General Biased Sampling Schemes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 217-227, March.
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    Cited by:

    1. Shen, Pao-sheng & Hsu, Huichen, 2020. "Conditional maximum likelihood estimation for semiparametric transformation models with doubly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Lior Rennert & Sharon X. Xie, 2022. "Cox regression model under dependent truncation," Biometrics, The International Biometric Society, vol. 78(2), pages 460-473, June.
    3. 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.

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