IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v24y2018i3d10.1007_s10985-017-9407-2.html
   My bibliography  Save this article

A joint model of cancer incidence, metastasis, and mortality

Author

Listed:
  • Qui Tran

    (University of Michigan)

  • Kelley M. Kidwell

    (University of Michigan)

  • Alex Tsodikov

    (University of Michigan)

Abstract

Many diseases, especially cancer, are not static, but rather can be summarized by a series of events or stages (e.g. diagnosis, remission, recurrence, metastasis, death). Most available methods to analyze multi-stage data ignore intermediate events and focus on the terminal event or consider (time to) multiple events as independent. Competing-risk or semi-competing-risk models are often deficient in describing the complex relationship between disease progression events which are driven by a shared progression stochastic process. A multi-stage model can only examine two stages at a time and thus fails to capture the effect of one stage on the time spent between other stages. Moreover, most models do not account for latent stages. We propose a semi-parametric joint model of diagnosis, latent metastasis, and cancer death and use nonparametric maximum likelihood to estimate covariate effects on the risks of intermediate events and death and the dependence between them. We illustrate the model with Monte Carlo simulations and analysis of real data on prostate cancer from the SEER database.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:lifeda:v:24:y:2018:i:3:d:10.1007_s10985-017-9407-2
    DOI: 10.1007/s10985-017-9407-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-017-9407-2
    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/s10985-017-9407-2?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. Lei Liu & Robert A. Wolfe & Xuelin Huang, 2004. "Shared Frailty Models for Recurrent Events and a Terminal Event," Biometrics, The International Biometric Society, vol. 60(3), pages 747-756, September.
    2. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    3. A. Tsodikov, 2003. "Semiparametric models: a generalized self‐consistency approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 759-774, August.
    4. Yi-Hau Chen, 2009. "Weighted Breslow-type and maximum likelihood estimation in semiparametric transformation models," Biometrika, Biometrika Trust, vol. 96(3), pages 591-600.
    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. Limin Peng & Jason P. Fine, 2007. "Regression Modeling of Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 63(1), pages 96-108, March.
    Full references (including those not matched with items on IDEAS)

    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. Xiaodong Luo & Hong Tian & Surya Mohanty & Wei Yann Tsai, 2015. "An alternative approach to confidence interval estimation for the win ratio statistic," Biometrics, The International Biometric Society, vol. 71(1), pages 139-145, March.
    2. Chia-Hui Huang, 2019. "Mixture regression models for the gap time distributions and illness–death processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 168-188, January.
    3. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    4. 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.
    5. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    6. John D. Rice & Alex Tsodikov, 2017. "Semiparametric time-to-event modeling in the presence of a latent progression event," Biometrics, The International Biometric Society, vol. 73(2), pages 463-472, June.
    7. 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.
    8. Xingqiu Zhao & Jie Zhou & Liuquan Sun, 2011. "Semiparametric Transformation Models with Time-Varying Coefficients for Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 67(2), pages 404-414, June.
    9. 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.
    10. 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.
    11. Yi‐Hau Chen, 2010. "Semiparametric marginal regression analysis for dependent competing risks under an assumed copula," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 235-251, March.
    12. P. G. Sankaran & P. Anisha, 2011. "Shared frailty model for recurrent event data with multiple causes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2859-2868, February.
    13. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
    14. C.-Y. Huang & J. Qin & M.-C. Wang, 2010. "Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative Censoring," Biometrics, The International Biometric Society, vol. 66(1), pages 39-49, March.
    15. Yang Li & Hao Liu & Xiaoshen Wang & Wanzhu Tu, 2022. "Semi‐parametric time‐to‐event modelling of lengths of hospital stays," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1623-1647, November.
    16. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    17. 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.
    18. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    19. Heuchenne, Cedric & Laurent, Stephane & Legrand, Catherine & Van Keilegom, Ingrid, 2011. "Likelihood based inference for semi-competing risks," LIDAM Discussion Papers ISBA 2011022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Lu Mao & D. Y. Lin, 2017. "Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 573-587, 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:3:d:10.1007_s10985-017-9407-2. 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.