IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p3929-3940.html

Analysis of length‐biased and partly interval‐censored survival data with mismeasured covariates

Author

Listed:
  • Li‐Pang Chen
  • Bangxu Qiu

Abstract

In this paper, we analyze the length‐biased and partly interval‐censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low‐dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high‐dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, we explore a valid inference method for handling high‐dimensional length‐biased and interval‐censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified.

Suggested Citation

  • Li‐Pang Chen & Bangxu Qiu, 2023. "Analysis of length‐biased and partly interval‐censored survival data with mismeasured covariates," Biometrics, The International Biometric Society, vol. 79(4), pages 3929-3940, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3929-3940
    DOI: 10.1111/biom.13898
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13898
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13898?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chen, Li-Pang, 2019. "Semiparametric estimation for cure survival model with left-truncated and right-censored data and covariate measurement error," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    2. Lianming Wang & Christopher S. McMahan & Michael G. Hudgens & Zaina P. Qureshi, 2016. "A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data," Biometrics, The International Biometric Society, vol. 72(1), pages 222-231, March.
    3. Jong S. Kim, 2003. "Maximum likelihood estimation for the proportional hazards model with partly interval‐censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 489-502, May.
    4. Tianxi Cai & Rebecca A. Betensky, 2003. "Hazard Regression for Interval-Censored Data with Penalized Spline," Biometrics, The International Biometric Society, vol. 59(3), pages 570-579, September.
    5. Wei Pan & Rick Chappell, 2002. "Estimation in the Cox Proportional Hazards Model with Left-Truncated and Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 64-70, March.
    6. Kuchenhoff, Helmut & Lederer, Wolfgang & Lesaffre, Emmanuel, 2007. "Asymptotic variance estimation for the misclassification SIMEX," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6197-6211, August.
    7. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2017. "Semiparametric estimation of the accelerated failure time model with partly interval‐censored data," Biometrics, The International Biometric Society, vol. 73(4), pages 1161-1168, December.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Peijie Wang & Danning Li & Jianguo Sun, 2021. "A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model," Biometrics, The International Biometric Society, vol. 77(4), pages 1303-1314, December.
    10. Pao-sheng Shen & Yingwei Peng & Hsin-Jen Chen & Chyong-Mei Chen, 2022. "Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 68-88, January.
    11. Wolfson, Julian, 2011. "EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 296-305.
    12. Hui Zhao & Qiwei Wu & Gang Li & Jianguo Sun, 2020. "Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 204-216, January.
    13. Cetinyurek-Yavuz, Aysun & Lambert, Philippe, 2011. "Smooth estimation of survival functions and hazard ratios from interval-censored data using Bayesian penalized B-splines," LIDAM Reprints ISBA 2011004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    15. Scolas, Sylvie & El Ghouch, Anouar & Legrand, Catherine & Oulhaj, Abderrahim, 2016. "Variable selection in a flexible parametric mixture cure model with interval-censored data," LIDAM Reprints ISBA 2016016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Huimin & Wang, Yilong & Bing, Heming & Wang, Shuying & Li, Niya, 2025. "Efficient regularized estimation of graphical proportional hazards model with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    2. Li-Pang Chen, 2026. "Variable selection via penalized ridge regression with error-prone variables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 78(2), pages 225-261, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    2. Mengyue Zhang & Shishu Zhao & Shuying Wang & Xiaolin Xu, 2025. "Regression analysis of a graphical proportional hazards model for informatively left-truncated current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(3), pages 498-542, July.
    3. Du, Mingyue & Zhao, Xingqiu & Sun, Jianguo, 2022. "Variable selection for case-cohort studies with informatively interval-censored outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    4. Tianyi Lu & Shuwei Li & Liuquan Sun, 2023. "Combined estimating equation approaches for the additive hazards model with left-truncated and interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 672-697, July.
    5. Rong Liu & Shishun Zhao & Tao Hu & Jianguo Sun, 2022. "Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 10(5), pages 1-18, February.
    6. Liang, Lixing & Zhuang, Yipeng & Yu, Philip L.H., 2024. "Variable selection for high-dimensional incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    7. Liuquan Sun & Shuwei Li & Lianming Wang & Xinyuan Song & Xuemei Sui, 2022. "Simultaneous variable selection in regression analysis of multivariate interval‐censored data," Biometrics, The International Biometric Society, vol. 78(4), pages 1402-1413, December.
    8. Fan Feng & Guanghui Cheng & Jianguo Sun, 2023. "Variable Selection for Length-Biased and Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 11(22), pages 1-20, November.
    9. Ying Wu & Richard J. Cook, 2015. "Penalized regression for interval‐censored times of disease progression: Selection of HLA markers in psoriatic arthritis," Biometrics, The International Biometric Society, vol. 71(3), pages 782-791, September.
    10. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
    11. Li-Pang Chen, 2022. "BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-23, October.
    12. Joseph Hadaya & Arjun Verma & Yas Sanaiha & Ramin Ramezani & Nida Qadir & Peyman Benharash, 2022. "Machine learning-based modeling of acute respiratory failure following emergency general surgery operations," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-13, April.
    13. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    14. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    15. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank, Research and Statistics Department.
    16. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    17. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    18. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    19. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    20. Hajime Shimao & Sung Joo Kim & Warut Khern-Am-Nuai & Maxime C. Cohen, 2025. "Revisiting the CEO Effect Through a Machine Learning Lens," Management Science, INFORMS, vol. 71(6), pages 5396-5408, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3929-3940. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.