IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v11y2019i3d10.1007_s12561-019-09258-y.html
   My bibliography  Save this article

Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model

Author

Listed:
  • Yixin Wang

    (Fred Hutchinson Cancer Research Center)

  • Ying Qing Chen

    (Fred Hutchinson Cancer Research Center)

Abstract

In population-based health research, the so-called population attributable fraction is an important quantity that calculates the percentage of excess risk of morbidity and mortality associated with modifiable risk factors for a given population. While the concept of “risk” is usually measured by event probabilities, in practice it may be of a more direct interest to know the excess life expectancy associated with the modifiable risk factors instead, particularly when mortality is of the ultimate concern. In this paper, we thus propose to study a novel quantity, termed “attributable life expectancy,” to measure the population attributable fraction of life expectancy. We further develop a model-based approach for the attributable life expectancy under the Oakes–Dasu proportional mean residual life model, and establish its asymptotic properties for inferences. Numerical studies that include Monte-Carlo simulations and an actual analysis of the mortality associated with smoking cessation in an Asia Cohort Consortium are conducted to evaluate the performance of our proposed method.

Suggested Citation

  • Yixin Wang & Ying Qing Chen, 2019. "Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 659-676, December.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09258-y
    DOI: 10.1007/s12561-019-09258-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-019-09258-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/s12561-019-09258-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. Y. Q. Chen & S. Cheng, 2006. "Linear life expectancy regression with censored data," Biometrika, Biometrika Trust, vol. 93(2), pages 303-313, June.
    2. Chen, Ying Qing, 2007. "Additive Expectancy Regression," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 153-166, March.
    3. Barry I. Graubard & Thomas R. Fears, 2005. "Standard Errors for Attributable Risk for Simple and Complex Sample Designs," Biometrics, The International Biometric Society, vol. 61(3), pages 847-855, September.
    4. Liuquan Sun & Xinyuan Song & Zhigang Zhang, 2012. "Mean residual life models with time-dependent coefficients under right censoring," Biometrika, Biometrika Trust, vol. 99(1), pages 185-197.
    5. Li Chen & D. Y. Lin & Donglin Zeng, 2010. "Attributable fraction functions for censored event times," Biometrika, Biometrika Trust, vol. 97(3), pages 713-726.
    6. Sun, Liuquan & Zhang, Zhigang, 2009. "A Class of Transformed Mean Residual Life Models With Censored Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 803-815.
    7. David C. Schmittlein & Donald G. Morrison, 1981. "The Median Residual Lifetime: A Characterization Theorem and an Application," Operations Research, INFORMS, vol. 29(2), pages 392-399, April.
    8. Y. Q. Chen & S. Cheng, 2005. "Semiparametric regression analysis of mean residual life with censored survival data," Biometrika, Biometrika Trust, vol. 92(1), pages 19-29, 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. Zahra Mansourvar & Torben Martinussen & Thomas H. Scheike, 2016. "An Additive–Multiplicative Restricted Mean Residual Life Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 487-504, June.
    2. Peng Jin & Anne Zeleniuch-Jacquotte & Mengling Liu, 2020. "Generalized mean residual life models for case-cohort and nested case-control studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 789-819, October.
    3. Ruiwen Zhou & Jianguo Sun, 2022. "Estimation of the Proportional Mean Residual Life Model with Internal and Longitudinal Covariates," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 550-563, December.
    4. Kyu Hyun Kim & Daniel J. Caplan & Sangwook Kang, 2023. "Smoothed quantile regression for censored residual life," Computational Statistics, Springer, vol. 38(2), pages 1001-1022, June.
    5. Peng Liu & Yixin Wang & Yong Zhou, 2015. "Quantile residual lifetime with right-censored and length-biased data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 999-1028, October.
    6. Wu, Hongping & Cao, Xiaomin & Du, Caifeng, 2019. "Estimating equations of additive mean residual life model with censored length-biased data," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    7. Zahra Mansourvar & Torben Martinussen & Thomas H. Scheike, 2015. "Semiparametric regression for restricted mean residual life under right censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2597-2613, December.
    8. Yang, Guangren & Zhou, Yong, 2014. "Semiparametric varying-coefficient study of mean residual life models," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 226-238.
    9. Xiaolin Chen & Qihua Wang, 2013. "Semiparametric proportional mean residual life model with covariates missing at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 647-663, September.
    10. V. N. Sreeja & P. G. Sankaran, 2007. "Proportional mean residual life model for gap time distributions of recurrent events," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 319-336.
    11. Ruosha Li & Xuelin Huang & Jorge Cortes, 2016. "Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 755-773, November.
    12. Junshan Shen & Wei Liang & Shuyuan He, 2012. "Likelihood ratio inference for mean residual life," Statistical Papers, Springer, vol. 53(2), pages 401-408, May.
    13. Shaked, Moshe, 2010. "The decreasing percentile residual life aging notion," DES - Working Papers. Statistics and Econometrics. WS ws101807, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Rosa E. Lillo, 2005. "On the median residual lifetime and its aging properties: A characterization theorem and applications," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(4), pages 370-380, June.
    15. Liliana Andriano & Christiaan W. S. Monden, 2019. "The Causal Effect of Maternal Education on Child Mortality: Evidence From a Quasi-Experiment in Malawi and Uganda," Demography, Springer;Population Association of America (PAA), vol. 56(5), pages 1765-1790, October.
    16. Pfeiffer, R.M. & Petracci, E., 2011. "Variance computations for functionals of absolute risk estimates," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 807-812, July.
    17. Cox, Christopher & Li, Xiuhong, 2012. "Model-based estimation of the attributable risk: A loglinear approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4180-4189.
    18. Xiaofeng Lv & Gupeng Zhang & Guangyu Ren, 2017. "Gini index estimation for lifetime data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 275-304, April.
    19. Jong-Hyeon Jeong & Sin-Ho Jung & Joseph P. Costantino, 2008. "Nonparametric Inference on Median Residual Life Function," Biometrics, The International Biometric Society, vol. 64(1), pages 157-163, March.
    20. Zhao, Mu & Jiang, Hongmei, 2015. "Berry–Esseen bounds for the percentile residual life function estimators," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 133-140.

    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:stabio:v:11:y:2019:i:3:d:10.1007_s12561-019-09258-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.