IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v111y2016i516p1543-1547.html
   My bibliography  Save this article

Rejoinder

Author

Listed:
  • Guanhua Chen
  • Donglin Zeng
  • Michael R. Kosorok

Abstract

No abstract is available for this item.

Suggested Citation

  • Guanhua Chen & Donglin Zeng & Michael R. Kosorok, 2016. "Rejoinder," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1543-1547, October.
  • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:516:p:1543-1547
    DOI: 10.1080/01621459.2016.1250573
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1250573
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2016.1250573?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. Erica E. M. Moodie & Thomas S. Richardson & David A. Stephens, 2007. "Demystifying Optimal Dynamic Treatment Regimes," Biometrics, The International Biometric Society, vol. 63(2), pages 447-455, June.
    2. Bibhas Chakraborty & Eric B. Laber & Yingqi Zhao, 2013. "Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme," Biometrics, The International Biometric Society, vol. 69(3), pages 714-723, September.
    3. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    4. Ying-Qi Zhao & Donglin Zeng & Eric B. Laber & Michael R. Kosorok, 2015. "New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 583-598, June.
    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. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    2. Yunan Wu & Lan Wang, 2021. "Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes," Biometrics, The International Biometric Society, vol. 77(2), pages 465-476, June.
    3. Yebin Tao & Lu Wang, 2017. "Adaptive contrast weighted learning for multi-stage multi-treatment decision-making," Biometrics, The International Biometric Society, vol. 73(1), pages 145-155, March.
    4. Xin Qiu & Donglin Zeng & Yuanjia Wang, 2018. "Estimation and evaluation of linear individualized treatment rules to guarantee performance," Biometrics, The International Biometric Society, vol. 74(2), pages 517-528, June.
    5. Peng Wu & Donglin Zeng & Haoda Fu & Yuanjia Wang, 2020. "On using electronic health records to improve optimal treatment rules in randomized trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1075-1086, December.
    6. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    7. Wei Liu & Zhiwei Zhang & Lei Nie & Guoxing Soon, 2017. "A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1381-1392, October.
    8. Kara E. Rudolph & Iván Díaz, 2022. "When the ends do not justify the means: Learning who is predicted to have harmful indirect effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 573-589, December.
    9. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
    10. Baqun Zhang & Min Zhang, 2018. "C‐learning: A new classification framework to estimate optimal dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 74(3), pages 891-899, September.
    11. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    12. Hongming Pu & Bo Zhang, 2021. "Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 318-345, April.
    13. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
    15. Qian Guan & Eric B. Laber & Brian J. Reich, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 936-942, July.
    16. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    17. Zhang, Haixiang & Huang, Jian & Sun, Liuquan, 2020. "A rank-based approach to estimating monotone individualized two treatment regimes," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    18. Michael P. Wallace & Erica E. M. Moodie, 2015. "Doubly‐robust dynamic treatment regimen estimation via weighted least squares," Biometrics, The International Biometric Society, vol. 71(3), pages 636-644, September.
    19. Zhilan Lou & Jun Shao & Menggang Yu, 2018. "Optimal treatment assignment to maximize expected outcome with multiple treatments," Biometrics, The International Biometric Society, vol. 74(2), pages 506-516, June.
    20. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    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:taf:jnlasa:v:111:y:2016:i:516:p:1543-1547. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.