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Covariate Bias Induced by Length-Biased Sampling of Failure Times

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  • Bergeron, Pierre-Jerome
  • Asgharian, Masoud
  • Wolfson, David B.

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  • Bergeron, Pierre-Jerome & Asgharian, Masoud & Wolfson, David B., 2008. "Covariate Bias Induced by Length-Biased Sampling of Failure Times," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 737-742, June.
    • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:june:p:737-742
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    Citations

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    Cited by:

    1. Yu Shen & Jing Ning & Jing Qin, 2017. "Nonparametric and semiparametric regression estimation for length-biased survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 3-24, January.
    2. Kuhn, Peter J. & Shen, Kailing, 2010. "Gender Discrimination in Job Ads: Theory and Evidence," IZA Discussion Papers 5195, Institute of Labor Economics (IZA).
    3. Jin Piao & Jing Ning & Yu Shen, 2019. "Semiparametric model for bivariate survival data subject to biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 409-429, April.
    4. Micha Mandel & Ya'akov Ritov, 2010. "The Accelerated Failure Time Model Under Biased Sampling," Biometrics, The International Biometric Society, vol. 66(4), pages 1306-1308, December.
    5. Bentoumi Rachid & Mesfioui Mhamed & Alvo Mayer, 2019. "Dependence measure for length-biased survival data using copulas," Dependence Modeling, De Gruyter, vol. 7(1), pages 348-364, January.
    6. Jieli Ding & Tsui-Shan Lu & Jianwen Cai & Haibo Zhou, 2017. "Recent progresses in outcome-dependent sampling with failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 57-82, January.
    7. Jing Ning & Jing Qin & Yu Shen, 2010. "Non‐parametric tests for right‐censored data with biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 609-630, November.
    8. Yu-Jen Cheng & Mei-Cheng Wang, 2012. "Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 707-716, September.
    9. 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.
    10. David E. Giles, 2021. "Improved Maximum Likelihood Estimation for the Weibull Distribution Under Length-Biased Sampling," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 59-77, December.
    11. Peter Kuhn & Kailing Shen, 2009. "Employers' Preferences for Gender, Age, Height and Beauty: Direct Evidence," NBER Working Papers 15564, National Bureau of Economic Research, Inc.
    12. Kwun Chuen Gary Chan & Mei-Cheng Wang, 2012. "Estimating Incident Population Distribution from Prevalent Data," Biometrics, The International Biometric Society, vol. 68(2), pages 521-531, June.
    13. Ertefaie Ashkan & Asgharian Masoud & Stephens David A., 2015. "Double Bias: Estimation of Causal Effects from Length-Biased Samples in the Presence of Confounding," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 69-89, May.
    14. Shih‐Wei Chen & Chin‐Tsang Chiang, 2018. "General single‐index survival regression models for incident and prevalent covariate data and prevalent data without follow‐up," Biometrics, The International Biometric Society, vol. 74(3), pages 881-890, September.

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