IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v29y2023i4d10.1007_s10985-023-09598-4.html
   My bibliography  Save this article

Improving marginal hazard ratio estimation using quadratic inference functions

Author

Listed:
  • Hongkai Liang

    (Dalian University of Technology)

  • Xiaoguang Wang

    (Dalian University of Technology)

  • Yingwei Peng

    (Queen’s University
    Queen’s University
    Queen’s Cancer Research Institute)

  • Yi Niu

    (Dalian University of Technology)

Abstract

Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.

Suggested Citation

  • Hongkai Liang & Xiaoguang Wang & Yingwei Peng & Yi Niu, 2023. "Improving marginal hazard ratio estimation using quadratic inference functions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 823-853, October.
  • Handle: RePEc:spr:lifeda:v:29:y:2023:i:4:d:10.1007_s10985-023-09598-4
    DOI: 10.1007/s10985-023-09598-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-023-09598-4
    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/s10985-023-09598-4?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. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Niu, Yi & Peng, Yingwei, 2015. "A new estimating equation approach for marginal hazard ratio estimation," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 46-56.
    3. Feifei Yan & Lin Zhu & Yanyan Liu & Jianwen Cai & Haibo Zhou, 2021. "Semiparametric regression based on quadratic inference function for multivariate failure time data with auxiliary information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 269-299, April.
    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. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    2. Bansal, Ravi & Kiku, Dana & Yaron, Amir, 2016. "Risks for the long run: Estimation with time aggregation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 52-69.
    3. Kutuk, Yasin, 2022. "Inequality convergence: A world-systems theory approach," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 150-165.
    4. Smoluk, H. J. & Neveu, Raymond P., 2002. "Consumption and asset prices: An analysis across income groups," Review of Financial Economics, Elsevier, vol. 11(1), pages 47-62.
    5. Alessandra Canepa & Fawaz Khaled, 2018. "Housing, Housing Finance and Credit Risk," IJFS, MDPI, vol. 6(2), pages 1-23, May.
    6. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84, pages 1571-1612, July.
    7. Jessica M. Mc Lay & Roy Lay-Yee & Barry J. Milne & Peter Davis, 2015. "Regression-Style Models for Parameter Estimation in Dynamic Microsimulation: An Empirical Performance Assessment," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 83-127.
    8. Jarle Aarstad & Olav Andreas Kvitastein & Stig-Erik Jakobsen, 2019. "What Drives Enterprise Product Innovation? Assessing How Regional, National, And International Inter-Firm Collaboration Complement Or Substitute For R&D Investments," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(05), pages 1-25, June.
    9. Alex Cukierman & Anton Muscatelli, 2001. "Do Central Banks have Precautionary Demands for Expansions and for Price Stability?," Working Papers 2002_4, Business School - Economics, University of Glasgow, revised Mar 2002.
    10. Li, Yuming, 1998. "Expected stock returns, risk premiums and volatilities of economic factors1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 69-97, June.
    11. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    12. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    13. Hansen, Lars Peter & Heaton, John & Luttmer, Erzo G J, 1995. "Econometric Evaluation of Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 237-274.
    14. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    15. Carranza, Luis J. & Cayo, Juan M. & Galdon-Sanchez, Jose E., 2003. "Exchange rate volatility and economic performance in Peru: a firm level analysis," Emerging Markets Review, Elsevier, vol. 4(4), pages 472-496, December.
    16. Aysit Tansel & Nil Demet Güngör, 2016. "Gender Effects of Education on Economic Development in Turkey," World Scientific Book Chapters, in: Nadereh Chamlou & Massoud Karshenas (ed.), Women, Work and Welfare in the Middle East and North Africa The Role of Socio-demographics, Entrepreneurship and Public Policies, chapter 3, pages 57-86, World Scientific Publishing Co. Pte. Ltd..
    17. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    18. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    19. Heng, Dyna, 2011. "Capital flows and real exchange rate: does financial development matter?," MPRA Paper 48553, University Library of Munich, Germany, revised May 2012.
    20. Chambers, Marcus J & Bailey, Roy E, 1996. "A Theory of Commodity Price Fluctuations," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 924-957, October.

    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:29:y:2023:i:4:d:10.1007_s10985-023-09598-4. 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.