IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v69y2017i5d10.1007_s10463-016-0580-y.html
   My bibliography  Save this article

Collapsibility of some association measures and survival models

Author

Listed:
  • P. Vellaisamy

    (Indian Institute of Technology Bombay)

Abstract

Collapsibility deals with the conditions under which a conditional (on a covariate W) measure of association between two random variables Y and X equals the marginal measure of association. In this paper, we discuss the average collapsibility of certain well-known measures of association, and also with respect to a new measure of association. The concept of average collapsibility is more general than collapsibility, and requires that the conditional average of an association measure equals the corresponding marginal measure. Sufficient conditions for the average collapsibility of the association measures are obtained. Some interesting counterexamples are constructed and applications to linear, Poisson, logistic and negative binomial regression models are discussed. An extension to the case of multivariate covariate W is also analyzed. Finally, we discuss the collapsibility conditions of some dependence measures for survival models and illustrate them for the case of linear transformation models.

Suggested Citation

  • P. Vellaisamy, 2017. "Collapsibility of some association measures and survival models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 1155-1176, October.
    • Handle: RePEc:spr:aistmt:v:69:y:2017:i:5:d:10.1007_s10463-016-0580-y
    DOI: 10.1007/s10463-016-0580-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10463-016-0580-y
    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/s10463-016-0580-y?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. Clelia Di Serio & Yosef Rinott & Marco Scarsini, 2009. "Simpson's Paradox in Survival Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 463-480, September.
    2. Zongming Ma & Xianchao Xie & Zhi Geng, 2006. "Collapsibility of distribution dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 127-133, February.
    3. D. R. Cox & Nanny Wermuth, 2003. "A general condition for avoiding effect reversal after marginalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 937-941, November.
    4. P. Vellaisamy, 2012. "Average Collapsibility of Distribution Dependence and Quantile Regression Coefficients," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(1), pages 153-165, March.
    5. A. N. Pettitt, 1984. "Proportional Odds Models for Survival Data and Estimates Using Ranks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(2), pages 169-175, June.
    6. P. Vellaisamy & V. Vijay, 2007. "Some collapsibility results for n-dimensional contingency tables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 557-576, September.
    7. D. R. Cox, 2003. "Conditional and marginal association for binary random variables," Biometrika, Biometrika Trust, vol. 90(4), pages 982-984, December.
    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. Nanny Wermuth & Kayvan Sadeghi, 2012. "Sequences of regressions and their independences," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 215-252, June.
    2. 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.
    3. P. Vellaisamy & V. Vijay, 2009. "Log-linear modeling using conditional log-linear structures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 309-329, June.
    4. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    5. Huang, Bin & Wang, Qihua, 2010. "Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2078-2090, October.
    6. 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.
    7. Jianbo Li & Minggao Gu & Tao Hu, 2012. "General partially linear varying-coefficient transformation models for ranking data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1475-1488, January.
    8. Ramesh Gupta & Cheng Peng, 2014. "Proportional odds frailty model and stochastic comparisons," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 897-912, October.
    9. Clelia Di Serio & Yosef Rinott & Marco Scarsini, 2009. "Simpson's Paradox in Survival Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 463-480, September.
    10. Xianchao Xie & Zhi Geng, 2009. "Collapsibility for Directed Acyclic Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 185-203, June.
    11. 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.
    12. Tyler J. VanderWeele & James M. Robins, 2010. "Signed directed acyclic graphs for causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 111-127, January.
    13. Xifen Huang & Chaosong Xiong & Tao Jiang & Junfeng Lu & Jinfeng Xu, 2022. "Efficient Estimation and Inference in the Proportional Odds Model for Survival Data," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    14. Ronghui Xu & David P. Harrington, 2001. "A Semiparametric Estimate of Treatment Effects with Censored Data," Biometrics, The International Biometric Society, vol. 57(3), pages 875-885, September.
    15. Vassilios Bazinas & Bent Nielsen, 2022. "Causal Transmission in Reduced-Form Models," Econometrics, MDPI, vol. 10(2), pages 1-25, March.
    16. Lu, Wenbin & Liang, Yu, 2006. "Empirical likelihood inference for linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1586-1599, August.
    17. Zhang, Hao Helen & Lu, Wenbin & Wang, Hansheng, 2010. "On sparse estimation for semiparametric linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1594-1606, August.
    18. Daniel Rabinowitz & Rebecca A. Betensky & Anastasios A. Tsiatis, 2000. "Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data," Biometrics, The International Biometric Society, vol. 56(2), pages 511-518, June.
    19. Zhao, Yichuan, 2010. "Semiparametric inference for transformation models via empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1846-1858, September.
    20. P. Vellaisamy & V. Vijay, 2007. "Some collapsibility results for n-dimensional contingency tables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 557-576, September.

    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:aistmt:v:69:y:2017:i:5:d:10.1007_s10463-016-0580-y. 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.