IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v24y2018i2d10.1007_s10985-016-9385-9.html
   My bibliography  Save this article

Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data

Author

Listed:
  • Chyong-Mei Chen

    (National Yang-Ming University)

  • Pao-Sheng Shen

    (Tunghai University)

Abstract

Left-truncated data often arise in epidemiology and individual follow-up studies due to a biased sampling plan since subjects with shorter survival times tend to be excluded from the sample. Moreover, the survival time of recruited subjects are often subject to right censoring. In this article, a general class of semiparametric transformation models that include proportional hazards model and proportional odds model as special cases is studied for the analysis of left-truncated and right-censored data. We propose a conditional likelihood approach and develop the conditional maximum likelihood estimators (cMLE) for the regression parameters and cumulative hazard function of these models. The derived score equations for regression parameter and infinite-dimensional function suggest an iterative algorithm for cMLE. The cMLE is shown to be consistent and asymptotically normal. The limiting variances for the estimators can be consistently estimated using the inverse of negative Hessian matrix. Intensive simulation studies are conducted to investigate the performance of the cMLE. An application to the Channing House data is given to illustrate the methodology.

Suggested Citation

  • Chyong-Mei Chen & Pao-Sheng Shen, 2018. "Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 250-272, April.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:2:d:10.1007_s10985-016-9385-9
    DOI: 10.1007/s10985-016-9385-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-016-9385-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10985-016-9385-9?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Micha Mandel & Rebecca A. Betensky, 2007. "Testing Goodness of Fit of a Uniform Truncation Model," Biometrics, The International Biometric Society, vol. 63(2), pages 405-412, June.
    2. Pao-sheng Shen, 2011. "Semiparametric analysis of transformation models with left-truncated and right-censored data," Computational Statistics, Springer, vol. 26(3), pages 521-537, September.
    3. Yu-Jen Cheng & Chiung-Yu Huang, 2014. "Combined estimating equation approaches for semiparametric transformation models with length-biased survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 608-618, September.
    4. 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.
    5. Donglin Zeng & D. Y. Lin, 2006. "Efficient estimation of semiparametric transformation models for counting processes," Biometrika, Biometrika Trust, vol. 93(3), pages 627-640, September.
    6. Pao-Sheng Shen, 2011. "Semiparametric analysis of transformation models with doubly censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 675-682, November.
    7. 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.
    8. Qian, Jing & Betensky, Rebecca A., 2014. "Assumptions regarding right censoring in the presence of left truncation," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 12-17.
    9. Kani Chen, 2002. "Semiparametric analysis of transformation models with censored data," Biometrika, Biometrika Trust, vol. 89(3), pages 659-668, August.
    10. Yi-Hau Chen, 2009. "Weighted Breslow-type and maximum likelihood estimation in semiparametric transformation models," Biometrika, Biometrika Trust, vol. 96(3), pages 591-600.
    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. 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. Prabhashi W. Withana Gamage & Christopher S. McMahan & Lianming Wang, 2023. "A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 188-212, January.

    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. Chyong-Mei Chen & Pao-sheng Shen & Yi Liu, 2021. "On semiparametric transformation model with LTRC data: pseudo likelihood approach," Statistical Papers, Springer, vol. 62(1), pages 3-30, February.
    2. 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.
    3. Qiu, Zhiping & Zhou, Yong, 2015. "Partially linear transformation models with varying coefficients for multivariate failure time data," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 144-166.
    4. Xi Ning & Yinghao Pan & Yanqing Sun & Peter B. Gilbert, 2023. "A semiparametric Cox–Aalen transformation model with censored data," Biometrics, The International Biometric Society, vol. 79(4), pages 3111-3125, December.
    5. Pao-sheng Shen, 2012. "Analysis of left-truncated right-censored or doubly censored data with linear transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 584-603, September.
    6. Yu-Jen Cheng & Chiung-Yu Huang, 2014. "Combined estimating equation approaches for semiparametric transformation models with length-biased survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 608-618, September.
    7. Sangbum Choi & Xuelin Huang, 2012. "A General Class of Semiparametric Transformation Frailty Models for Nonproportional Hazards Survival Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1126-1135, December.
    8. Pao-sheng Shen, 2014. "Semiparametric regression analysis for clustered doubly-censored data," Computational Statistics, Springer, vol. 29(3), pages 813-828, June.
    9. Qui Tran & Kelley M. Kidwell & Alex Tsodikov, 2018. "A joint model of cancer incidence, metastasis, and mortality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 385-406, July.
    10. Pao-sheng Shen & Yi Liu, 2019. "Pseudo maximum likelihood estimation for the Cox model with doubly truncated data," Statistical Papers, Springer, vol. 60(4), pages 1207-1224, August.
    11. Zhiping Qiu & Jing Qin & Yong Zhou, 2016. "Composite Estimating Equation Method for the Accelerated Failure Time Model with Length-biased Sampling Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 396-415, June.
    12. Yu-Jen Cheng & Mei-Cheng Wang, 2015. "Causal estimation using semiparametric transformation models under prevalent sampling," Biometrics, The International Biometric Society, vol. 71(2), pages 302-312, June.
    13. Wang, Qihua & Tong, Xingwei & Sun, Liuquan, 2012. "Exploring the varying covariate effects in proportional odds models with censored data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 168-189.
    14. Sangbum Choi & Xuelin Huang, 2014. "Maximum likelihood estimation of semiparametric mixture component models for competing risks data," Biometrics, The International Biometric Society, vol. 70(3), pages 588-598, September.
    15. Min Zhang & Marie Davidian, 2008. "“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data," Biometrics, The International Biometric Society, vol. 64(2), pages 567-576, June.
    16. Chi-Chung Wen & Yi-Hau Chen, 2014. "Semiparametric analysis of incomplete current status outcome data under transformation models," Biometrics, The International Biometric Society, vol. 70(2), pages 335-345, June.
    17. Isabel Proença & Horácio Faustino, 2015. "Modelling bilateral intra-industry trade indexes with panel data: a semiparametric approach," Computational Statistics, Springer, vol. 30(3), pages 865-884, September.
    18. 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.
    19. Pao-sheng Shen, 2011. "Semiparametric analysis of transformation models with left-truncated and right-censored data," Computational Statistics, Springer, vol. 26(3), pages 521-537, September.
    20. 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.

    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:spr:lifeda:v:24:y:2018:i:2:d:10.1007_s10985-016-9385-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.