IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v71y2019i4d10.1007_s10463-018-0667-8.html
   My bibliography  Save this article

Bias reduction using surrogate endpoints as auxiliary variables

Author

Listed:
  • Yoshiharu Takagi

    (Sanofi K.K.)

  • Yutaka Kano

    (Osaka University)

Abstract

Recently, it is becoming more active to apply appropriate statistical methods dealing with missing data in clinical trials. Under not missing at random missingness, MLE based on direct-likelihood, or observed likelihood, possibly has a serious bias. A solution to the bias problem is to add auxiliary variables such as surrogate endpoints to the model for the purpose of reducing the bias. We theoretically studied the impact of an auxiliary variable on MLE and evaluated the bias reduction or inflation in the case of several typical correlation structures.

Suggested Citation

  • Yoshiharu Takagi & Yutaka Kano, 2019. "Bias reduction using surrogate endpoints as auxiliary variables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 837-852, August.
    • Handle: RePEc:spr:aistmt:v:71:y:2019:i:4:d:10.1007_s10463-018-0667-8
    DOI: 10.1007/s10463-018-0667-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10463-018-0667-8
    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/s10463-018-0667-8?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. Yun Li & Jeremy M. G. Taylor & Roderick J. A. Little, 2011. "A Shrinkage Approach for Estimating a Treatment Effect Using Intermediate Biomarker Data in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(4), pages 1434-1441, December.
    2. Joseph G. Ibrahim & Stuart R. Lipsitz & Nick Horton, 2001. "Using auxiliary data for parameter estimation with non‐ignorably missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 361-373.
    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. Liang, Hua & Su, Haiyan & Zou, Guohua, 2008. "Confidence intervals for a common mean with missing data with applications in an AIDS study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 546-553, December.
    2. Layla Parast & Beth Ann Griffin, 2017. "Landmark estimation of survival and treatment effects in observational studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 161-182, April.
    3. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    4. Lei Wang & Wei Ma, 2021. "Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 623-647, June.
    5. Liang, Hua, 2008. "Generalized partially linear models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 880-895, May.
    6. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
    7. Dan Jackson & Ian R. White & Morven Leese, 2010. "How much can we learn about missing data?: an exploration of a clinical trial in psychiatry," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 593-612, July.
    8. Shonosuke Sugasawa & Kosuke Morikawa & Keisuke Takahata, 2022. "Bayesian semiparametric modeling of response mechanism for nonignorable missing data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 101-117, March.
    9. Wang Miao & Eric J. Tchetgen Tchetgen, 2016. "On varieties of doubly robust estimators under missingness not at random with a shadow variable," Biometrika, Biometrika Trust, vol. 103(2), pages 475-482.
    10. A. S. C. Conlon & J. M. G. Taylor & D. J. Sargent, 2015. "Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model," Biometrics, The International Biometric Society, vol. 71(2), pages 460-468, June.

    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:71:y:2019:i:4:d:10.1007_s10463-018-0667-8. 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.