IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v28y2022i4d10.1007_s10985-022-09570-8.html
   My bibliography  Save this article

Median regression models for clustered, interval-censored survival data - An application to prostate surgery study

Author

Listed:
  • Debajyoti Sinha

    (Florida State University)

  • Piyali Basak

    (Merck Research Laboratory)

  • Stuart R. Lipsitz

    (Brigham & Women’s Hospital)

Abstract

Genitourinary surgeons and oncologists are particularly interested in whether a robotic surgery improves times to Prostate Specific Antigen (PSA) recurrence compared to a non-robotic surgery for removing the cancerous prostate. Time to PSA recurrence is an example of a survival time that is typically interval-censored between two consecutive clinical inspections with opposite test results. In addition, success of medical devices and technologies often depends on factors such as experience and skill level of the medical service providers, thus leading to clustering of these survival times. For analyzing the effects of surgery types and other covariates on median of clustered interval-censored time to post-surgery PSA recurrence, we present three competing novel models and associated frequentist and Bayesian analyses. The first model is based on a transform-both-sides of survival time with Gaussian random effects to account for the within-cluster association. Our second model assumes an approximate marginal Laplace distribution for the transformed log-survival times with a Gaussian copula to accommodate clustering. Our third model is a special case of the second model with Laplace distribution for the marginal log-survival times and Gaussian copula for the within-cluster association. Simulation studies establish the second model to be highly robust against extreme observations while estimating median regression coefficients. We provide a comprehensive comparison among these three competing models based on the model properties and the computational ease of their Frequentist and Bayesian analysis. We also illustrate the practical implementations and uses of these methods via analysis of a simulated clustered interval-censored data-set similar in design to a post-surgery PSA recurrence study.

Suggested Citation

  • Debajyoti Sinha & Piyali Basak & Stuart R. Lipsitz, 2022. "Median regression models for clustered, interval-censored survival data - An application to prostate surgery study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 723-743, October.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:4:d:10.1007_s10985-022-09570-8
    DOI: 10.1007/s10985-022-09570-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-022-09570-8
    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/s10985-022-09570-8?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. Parente Paulo M.D.C. & Santos Silva João M.C., 2016. "Quantile Regression with Clustered Data," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 1-15, January.
    2. Bernd Fitzenberger & Ralf Wilke, 2006. "Using quantile regression for duration analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 105-120, March.
    3. Jianchang Lin & Debajyoti Sinha & Stuart Lipsitz & Adriano Polpo, 2012. "Semiparametric Bayesian Survival Analysis using Models with Log-linear Median," Biometrics, The International Biometric Society, vol. 68(4), pages 1136-1145, December.
    4. Fitzmaurice, Garrett M. & Lipsitz, Stuart R. & Parzen, Michael, 2007. "Approximate Median Regression via the Box-Cox Transformation," The American Statistician, American Statistical Association, vol. 61, pages 233-238, August.
    5. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Timothy Hanson & Mingan Yang, 2007. "Bayesian Semiparametric Proportional Odds Models," Biometrics, The International Biometric Society, vol. 63(1), pages 88-95, 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. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    2. Chen, Songnian, 2023. "Two-step estimation of censored quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 235(2), pages 1310-1336.
    3. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    4. repec:iab:iabfme:200709(en is not listed on IDEAS
    5. Sungwan Bang & Soo-Heang Eo & Yong Mee Cho & Myoungshic Jhun & HyungJun Cho, 2016. "Non-crossing weighted kernel quantile regression with right censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 100-121, January.
    6. Katarzyna Burzynska & Olle Berggren, 2015. "The Impact of Social Beliefs on Microfinance Performance," Journal of International Development, John Wiley & Sons, Ltd., vol. 27(7), pages 1074-1097, October.
    7. Aysa Ipek Erdogan, 2023. "Drivers of SME Growth: Quantile Regression Evidence From Developing Countries," SAGE Open, , vol. 13(1), pages 21582440231, March.
    8. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    9. Valentine Fays & Benoît Mahy & François Rycx, 2023. "Wage differences according to workers' origin: The role of working more upstream in GVCs," LABOUR, CEIS, vol. 37(2), pages 319-342, June.
    10. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1275-1306, December.
    11. Cummins, Joseph R., 2017. "Heterogeneous treatment effects in the low track: Revisiting the Kenyan primary school experiment," Economics of Education Review, Elsevier, vol. 56(C), pages 40-51.
    12. Markus Grillitsch & Magnus Nilsson, 2019. "Knowledge externalities and firm heterogeneity: Effects on high and low growth firms," Papers in Regional Science, Wiley Blackwell, vol. 98(1), pages 93-114, February.
    13. Wilke, Ralf A. & Wichert, Laura, 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67 [rev.], ZEW - Leibniz Centre for European Economic Research.
    14. Bernd Fitzenberger & Ralf Wilke, 2006. "Using quantile regression for duration analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 105-120, March.
    15. Opoku, Eric Evans Osei & Aluko, Olufemi Adewale, 2021. "Heterogeneous effects of industrialization on the environment: Evidence from panel quantile regression," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 174-184.
    16. Opoku, Eric Evans Osei & Dogah, Kingsley E. & Aluko, Olufemi Adewale, 2022. "The contribution of human development towards environmental sustainability," Energy Economics, Elsevier, vol. 106(C).
    17. Amal Jmaii & Damien Rousselière & Christophe Daniel, 2017. "Semi†parametric Regression†based Decomposition Methods: Evidence from Regional Inequality in Tunisia," African Development Review, African Development Bank, vol. 29(4), pages 660-673, December.
    18. Luigi Aldieri & Concetto Paolo Vinci, 2017. "Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 106-113, July.
    19. Jooyong Shim & Changha Hwang & Kyungha Seok, 2014. "Composite support vector quantile regression estimation," Computational Statistics, Springer, vol. 29(6), pages 1651-1665, December.
    20. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2014. "A new quantile regression forecasting model," Journal of Business Research, Elsevier, vol. 67(5), pages 779-784.
    21. Giovanni Dosi & Dario Guarascio & Andrea Ricci & Maria Enrica Virgillito, 2021. "Neodualism in the Italian business firms: training, organizational capabilities, and productivity distributions," Small Business Economics, Springer, vol. 57(1), pages 167-189, June.

    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:28:y:2022:i:4:d:10.1007_s10985-022-09570-8. 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.