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A Pseudoscore Estimator for Regression Problems With Two-Phase Sampling

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  • Chatterjee N.
  • Chen Y-H.
  • Breslow N.E.

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  • Chatterjee N. & Chen Y-H. & Breslow N.E., 2003. "A Pseudoscore Estimator for Regression Problems With Two-Phase Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 158-168, January.
    • Handle: RePEc:bes:jnlasa:v:98:y:2003:p:158-168
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    Cited by:

    1. James Y. Dai & Michael LeBlanc & Charles Kooperberg, 2009. "Semiparametric Estimation Exploiting Covariate Independence in Two-Phase Randomized Trials," Biometrics, The International Biometric Society, vol. 65(1), pages 178-187, March.
    2. Zhiwei Zhang & Howard Rockette, 2006. "Semiparametric Maximum Likelihood for Missing Covariates in Parametric Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(4), pages 687-706, December.
    3. Lawrence C. McCandless & Sylvia Richardson & Nicky Best, 2012. "Adjustment for Missing Confounders Using External Validation Data and Propensity Scores," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 40-51, March.
    4. X. Hu & Bin Zhang, 2012. "Pseudolikelihood ratio test with biased observations," Statistical Papers, Springer, vol. 53(2), pages 387-400, May.
    5. 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.
    6. Haibo Zhou & Guoyou Qin & Matthew P. Longnecker, 2011. "A Partial Linear Model in the Outcome-Dependent Sampling Setting to Evaluate the Effect of Prenatal PCB Exposure on Cognitive Function in Children," Biometrics, The International Biometric Society, vol. 67(3), pages 876-885, September.
    7. Qingning Zhou & Jianwen Cai & Haibo Zhou, 2018. "Outcome†dependent sampling with interval†censored failure time data," Biometrics, The International Biometric Society, vol. 74(1), pages 58-67, March.
    8. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    9. Belitskaya-Levy Ilana & Shao Yongzhao & Goldberg Judith D, 2008. "Systematic Missing-At-Random (SMAR) Design and Analysis for Translational Research Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, July.
    10. Qingning Zhou & Jianwen Cai & Haibo Zhou, 2020. "Semiparametric inference for a two-stage outcome-dependent sampling design with interval-censored failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 85-108, January.
    11. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    12. J. F. Lawless, 2018. "Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 28-44, January.
    13. Christopher Vahl & Qing Kang, 2015. "Analysis of an outcome-dependent enriched sample: hypothesis tests," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 387-409, September.
    14. Ying Huang & Peter B. Gilbert & Julian Wolfson, 2013. "Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 301-309, June.
    15. S. Haneuse & J. Chen, 2011. "A Multiphase Design Strategy for Dealing with Participation Bias," Biometrics, The International Biometric Society, vol. 67(1), pages 309-318, March.
    16. Ying Huang, 2018. "Evaluating principal surrogate markers in vaccine trials in the presence of multiphase sampling," Biometrics, The International Biometric Society, vol. 74(1), pages 27-39, March.
    17. Samiran Sinha & Krishna K. Saha & Suojin Wang, 2014. "Semiparametric approach for non-monotone missing covariates in a parametric regression model," Biometrics, The International Biometric Society, vol. 70(2), pages 299-311, June.
    18. Yang Zhao & Meng Liu, 2021. "Unified approach for regression models with nonmonotone missing at random data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 87-101, March.
    19. Haibo Zhou & Rui Song & Yuanshan Wu & Jing Qin, 2011. "Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome," Biometrics, The International Biometric Society, vol. 67(1), pages 194-202, March.
    20. Weiwei Wang & Daniel Scharfstein & Zhiqiang Tan & Ellen J. MacKenzie, 2009. "Causal inference in outcome‐dependent two‐phase sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 947-969, November.
    21. Fatema Shafie Khorassani & Jeremy M. G. Taylor & Niko Kaciroti & Michael R. Elliott, 2023. "Incorporating Covariates into Measures of Surrogate Paradox Risk," Stats, MDPI, vol. 6(1), pages 1-23, February.
    22. Yang Zhao, 2021. "Semiparametric model for regression analysis with nonmonotone missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 461-475, June.

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