IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i4p1110-1121.html
   My bibliography  Save this article

Multivariate exponential survival trees and their application to tooth prognosis

Author

Listed:
  • Fan, Juanjuan
  • Nunn, Martha E.
  • Su, Xiaogang

Abstract

This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of [LeBlanc, M., Crowley, J., 1993. Survival trees by goodness of split. Journal of the American Statistical Association 88, 457-467] to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in [Breiman, L., 2001. Random forests. Machine Learning 45, 5-32]. Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogeneous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations.

Suggested Citation

  • Fan, Juanjuan & Nunn, Martha E. & Su, Xiaogang, 2009. "Multivariate exponential survival trees and their application to tooth prognosis," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1110-1121, February.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1110-1121
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00478-7
    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. Gao, Feng & Manatunga, Amita K. & Chen, Shande, 2004. "Identification of prognostic factors with multivariate survival data," Computational Statistics & Data Analysis, Elsevier, vol. 45(4), pages 813-824, May.
    2. Ciampi, Antonio & Thiffault, Johanne & Nakache, Jean-Pierre & Asselain, Bernard, 1986. "Stratification by stepwise regression, correspondence analysis and recursive partition: a comparison of three methods of analysis for survival data with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 4(3), pages 185-204, October.
    3. Xiaogang Su & Juanjuan Fan, 2004. "Multivariate Survival Trees: A Maximum Likelihood Approach Based on Frailty Models," Biometrics, The International Biometric Society, vol. 60(1), pages 93-99, 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. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2015. "Tree-based censored regression with applications to insurance," Working Papers hal-01141228, HAL.
    2. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2016. "Tree-based censored regression with applications in insurance," Post-Print hal-01141228, HAL.
    3. Rancoita, Paola M.V. & Zaffalon, Marco & Zucca, Emanuele & Bertoni, Francesco & de Campos, Cassio P., 2016. "Bayesian network data imputation with application to survival tree analysis," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 373-387.

    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. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    2. Xiaogang Su & Juanjuan Fan, 2004. "Multivariate Survival Trees: A Maximum Likelihood Approach Based on Frailty Models," Biometrics, The International Biometric Society, vol. 60(1), pages 93-99, March.
    3. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2015. "Tree-based censored regression with applications to insurance," Working Papers hal-01141228, HAL.
    4. Un Jung Lee & ShengLi Tzeng & Yu-Chuan Chen & James J Chen, 2017. "Development of Predictive Signatures for Treatment Selection in Precision Medicine," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 2(4), pages 83-88, August.
    5. Yan Zhou & John McArdle, 2015. "Rationale and Applications of Survival Tree and Survival Ensemble Methods," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 811-833, September.
    6. Besse, Philippe & Leconte, Eve & Walschaerts, Marie, 2012. "Stable variable selection for right censored data: comparison of methods," TSE Working Papers 12-486, Toulouse School of Economics (TSE).
    7. Olivier Lopez & Xavier Milhaud & Pierre-Emmanuel Thérond, 2016. "Tree-based censored regression with applications in insurance," Post-Print hal-01141228, HAL.
    8. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    9. Hua Jin & Ying Lu & Kaite Stone & Dennis M. Black, 2004. "Alternative Tree-Structured Survival Analysis Based on Variance of Survival Time," Medical Decision Making, , vol. 24(6), pages 670-680, November.
    10. Zhang, Heping, 2004. "Recursive Partitioning and Tree-based Methods," Papers 2004,30, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    11. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    12. Lore Zumeta-Olaskoaga & Maximilian Weigert & Jon Larruskain & Eder Bikandi & Igor Setuain & Josean Lekue & Helmut Küchenhoff & Dae-Jin Lee, 2023. "Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 101-126, March.
    13. A. S. Foulkes & V. De Gruttola, 2002. "Characterizing the Relationship Between HIV-1 Genotype and Phenotype: Prediction-Based Classification," Biometrics, The International Biometric Society, vol. 58(1), pages 145-156, March.
    14. Peter Calhoun & Richard A. Levine & Juanjuan Fan, 2021. "Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia," Biometrics, The International Biometric Society, vol. 77(1), pages 343-351, March.
    15. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.
    16. Yifei Sun & Sy Han Chiou & Mei‐Cheng Wang, 2020. "ROC‐guided survival trees and ensembles," Biometrics, The International Biometric Society, vol. 76(4), pages 1177-1189, December.
    17. Karen Lostritto & Robert L. Strawderman & Annette M. Molinaro, 2012. "A Partitioning Deletion/Substitution/Addition Algorithm for Creating Survival Risk Groups," Biometrics, The International Biometric Society, vol. 68(4), pages 1146-1156, December.
    18. Rancoita, Paola M.V. & Zaffalon, Marco & Zucca, Emanuele & Bertoni, Francesco & de Campos, Cassio P., 2016. "Bayesian network data imputation with application to survival tree analysis," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 373-387.
    19. Hua Jin & Ying Lu, 2011. "Cost-Saving Tree-Structured Survival Analysis for Hip Fracture of Study of Osteoporotic Fractures Data," Medical Decision Making, , vol. 31(2), pages 299-307, March.

    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:4:p:1110-1121. 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.