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Estimating Individualized Treatment Rules Using Outcome Weighted Learning

Author

Listed:
  • Yingqi Zhao
  • Donglin Zeng
  • A. John Rush
  • Michael R. Kosorok

Abstract

There is increasing interest in discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal ITR that is a deterministic function of patient-specific characteristics maximizing expected clinical outcome. In this article, we first show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome. We then propose an outcome weighted learning approach based on the support vector machine framework. We show that the resulting estimator of the treatment rule is consistent. We further obtain a finite sample bound for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1106-1118
    DOI: 10.1080/01621459.2012.695674
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    Cited by:

    1. repec:spr:lifeda:v:23:y:2017:i:4:d:10.1007_s10985-016-9376-x is not listed on IDEAS
    2. Susan Athey & Stefan Wager, 2017. "Efficient Policy Learning," Papers 1702.02896, arXiv.org, revised Oct 2017.
    3. Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Papers 1709.10279, arXiv.org, revised May 2018.
    4. 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.
    5. Eric B. Laber & Anastasios A. Tsiatis & Marie Davidian & Shannon T. Holloway, 2014. "Discussion of “Combining biomarkers to optimize patient treatment recommendation”," Biometrics, The International Biometric Society, vol. 70(3), pages 707-710, September.
    6. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 0. "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. 0, pages 1-20.
    7. Ying Huang & Youyi Fong, 2014. "Identifying optimal biomarker combinations for treatment selection via a robust kernel method," Biometrics, The International Biometric Society, vol. 70(4), pages 891-901, December.
    8. repec:bla:jorssb:v:79:y:2017:i:4:p:1165-1185 is not listed on IDEAS
    9. repec:bla:biomet:v:73:y:2017:i:2:p:391-400 is not listed on IDEAS
    10. repec:bla:jorssc:v:66:y:2017:i:2:p:345-361 is not listed on IDEAS
    11. Rubin Daniel B. & van der Laan Mark J., 2012. "Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-20, July.
    12. Leo Guelman & Montserrat Guillen & Ana M. Pérez-Marín, 2014. "Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study," Working Papers 2014-06, Universitat de Barcelona, UB Riskcenter.
    13. 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.
    14. repec:bla:biomet:v:73:y:2017:i:1:p:145-155 is not listed on IDEAS
    15. Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 695-707, September.
    16. Eric B. Laber & Daniel J. Lizotte & Bradley Ferguson, 2014. "Set-valued dynamic treatment regimes for competing outcomes," Biometrics, The International Biometric Society, vol. 70(1), pages 53-61, March.
    17. repec:bla:jorssb:v:79:y:2017:i:5:p:1565-1582 is not listed on IDEAS
    18. repec:bla:biomet:v:72:y:2016:i:4:p:1017-1025 is not listed on IDEAS
    19. Roland A. Matsouaka & Junlong Li & Tianxi Cai, 2014. "Evaluating marker-guided treatment selection strategies," Biometrics, The International Biometric Society, vol. 70(3), pages 489-499, September.
    20. Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Rejoinder: Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 719-720, September.
    21. Ying-Qi Zhao & Michael R. Kosorok, 2014. "Discussion of combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 713-716, September.
    22. repec:jss:jstsof:v:080:i02 is not listed on IDEAS
    23. Ying Huang & Eric Laber, 2016. "Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 43-65, June.

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