IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i11p3864-3871.html
   My bibliography  Save this article

Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data

Author

Listed:
  • Shih, Joanna H.
  • Lu, Shou-En

Abstract

Multi-level clustered failure time data arise when the clustering of data occurs at more than one level. It is of interest to estimate the relative risks of covariates and clustering effect of failure times at each level. We consider a nested random effect proportional hazards model, where a subcluster-specific frailty operates multiplicatively on the conditional hazard model, and its distribution function depends on a cluster-specific random frailty. Under this model, we propose a Monte-Carlo EM-based semiparametric estimation procedure to estimate regression coefficients, nonparametric baseline cumulative hazard and the association parameters. In addition, we derive a covariance matrix of the parameter estimates. We illustrate the proposed method using clustered survival data collected from a vitamin A supplementation trial in Nepal, where it is of scientific interest to assess the clustering effect of mortality within households and within villages. We use simulations to study the performance of the proposed estimation procedure.

Suggested Citation

  • Shih, Joanna H. & Lu, Shou-En, 2009. "Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3864-3871, September.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:11:p:3864-3871
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00166-2
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Samuel Manda & Renate Meyer, 2005. "Age at first marriage in Malawi: a Bayesian multilevel analysis using a discrete time‐to‐event model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 439-455, March.
    2. Renjun Ma, 2003. "Random effects Cox models: A Poisson modelling approach," Biometrika, Biometrika Trust, vol. 90(1), pages 157-169, March.
    3. Joe, H., 1993. "Parametric Families of Multivariate Distributions with Given Margins," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 262-282, August.
    4. Joanna H. Shih & Shou-En Lu, 2007. "Analysis of Failure Time Data with Multilevel Clustering, with Application to the Child Vitamin A Intervention Trial in Nepal," Biometrics, The International Biometric Society, vol. 63(3), pages 673-680, September.
    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. Lee, Woojoo & Shi, Jian Qing & Lee, Youngjo, 2010. "Approximate conditional inference in mixed-effects models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 173-184, 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. Mirza Nazmul Hasan & Roel Braekers, 2021. "Estimation of the association parameters in hierarchically clustered survival data by nested Archimedean copula functions," Computational Statistics, Springer, vol. 36(4), pages 2755-2787, December.
    2. Joanna H. Shih & Shou-En Lu, 2007. "Analysis of Failure Time Data with Multilevel Clustering, with Application to the Child Vitamin A Intervention Trial in Nepal," Biometrics, The International Biometric Society, vol. 63(3), pages 673-680, September.
    3. Bedoui, Rihab & Braiek, Sana & Guesmi, Khaled & Chevallier, Julien, 2019. "On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model," Energy Economics, Elsevier, vol. 80(C), pages 876-889.
    4. Zhang, Dalu, 2014. "Vine copulas and applications to the European Union sovereign debt analysis," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 46-56.
    5. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    6. Guillaume Horny & Rute Mendes & Gerard J. Van den Berg, 2006. "Job mobility in Portugal: a Bayesian study with matched worker-firm data," Working Papers of BETA 2006-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    7. Y. Malevergne & D. Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 231-250.
    8. Yu, Lining & Voit, Eberhard O., 2006. "Construction of bivariate S-distributions with copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1822-1839, December.
    9. Nabil Kazi-Tani & Didier Rullière, 2019. "On a construction of multivariate distributions given some multidimensional marginals," Post-Print hal-01575169, HAL.
    10. Ying Hung & Li‐Hsiang Lin & C. F. Jeff Wu, 2022. "Varying coefficient frailty models with applications in single molecular experiments," Biometrics, The International Biometric Society, vol. 78(2), pages 474-486, June.
    11. Aristidis K. Nikoloulopoulos & Peter G. Moffatt, 2019. "Coupling Couples With Copulas: Analysis Of Assortative Matching On Risk Attitude," Economic Inquiry, Western Economic Association International, vol. 57(1), pages 654-666, January.
    12. Mika Ueyama & Futoshi Yamauchi, 2009. "Marriage behavior response to prime-age adult mortality: evidence from malawi," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 43-63, February.
    13. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    14. Yuri Salazar & Wing Ng, 2015. "Nonparametric estimation of general multivariate tail dependence and applications to financial time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 121-158, March.
    15. Sammy Khagayi & Nyaguara Amek & Godfrey Bigogo & Frank Odhiambo & Penelope Vounatsou, 2017. "Bayesian spatio-temporal modeling of mortality in relation to malaria incidence in Western Kenya," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    16. Emmanuel Afuecheta & Saralees Nadarajah & Stephen Chan, 2021. "A Statistical Analysis of Global Economies Using Time Varying Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1167-1194, December.
    17. L. J. Welty & R. D. Peng & S. L. Zeger & F. Dominici, 2009. "Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality," Biometrics, The International Biometric Society, vol. 65(1), pages 282-291, March.
    18. Dalla Valle Luciana, 2016. "The Use of Official Statistics in Self-Selection Bias Modeling," Journal of Official Statistics, Sciendo, vol. 32(4), pages 887-905, December.
    19. Jianxi Su & Edward Furman, 2016. "Multiple risk factor dependence structures: Copulas and related properties," Papers 1610.02126, arXiv.org.
    20. Luca Zanin & Rosalba Radice & Giampiero Marra, 2015. "Modelling the impact of women’s education on fertility in Malawi," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(1), pages 89-111, January.

    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:eee:csdana:v:53:y:2009:i:11:p:3864-3871. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.