IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/102112.html
   My bibliography  Save this paper

Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects

Author

Listed:
  • Shi, Chengchun
  • Song, Rui
  • Lu, Wenbin
  • Fu, Bo

Abstract

A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients’ heterogeneity caused by groupwise individualized treatment effects assuming the same marginal treatment effects for all groups. We propose a new maximin projection learning method for estimating a single treatment decision rule that works reliably for a group of future patients from a possibly new subpopulation. Based on estimated optimal treatment regimes for all subgroups, the proposed maximin treatment regime is obtained by solving a quadratically constrained linear programming problem, which can be efficiently computed by interior point methods. Consistency and asymptotic normality of the estimator are established. Numerical examples show the reliability of the methodology proposed.

Suggested Citation

  • Shi, Chengchun & Song, Rui & Lu, Wenbin & Fu, Bo, 2018. "Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects," LSE Research Online Documents on Economics 102112, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:102112
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/102112/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    2. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    3. Caiyun Fan & Wenbin Lu & Rui Song & Yong Zhou, 2017. "Concordance-assisted learning for estimating optimal individualized treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1565-1582, November.
    4. Han Chen & Alisa K. Manning & Josée Dupuis, 2012. "A Method of Moments Estimator for Random Effect Multivariate Meta-Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1278-1284, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shi, Chengchun & Wan, Runzhe & Song, Ge & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2023. "A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets," LSE Research Online Documents on Economics 117174, London School of Economics and Political Science, LSE Library.
    2. Li, Ting & Shi, Chengchun & Wen, Qianglin & Sui, Yang & Qin, Yongli & Lai, Chunbo & Zhu, Hongtu, 2024. "Combining experimental and historical data for policy evaluation," LSE Research Online Documents on Economics 125588, London School of Economics and Political Science, LSE Library.
    3. Chengchun Shi & Sheng Zhang & Wenbin Lu & Rui Song, 2022. "Statistical inference of the value function for reinforcement learning in infinite‐horizon settings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 765-793, July.
    4. Shi, Chengchun & Luo, Shikai & Le, Yuan & Zhu, Hongtu & Song, Rui, 2022. "Statistically efficient advantage learning for offline reinforcement learning in infinite horizons," LSE Research Online Documents on Economics 115598, London School of Economics and Political Science, LSE Library.
    5. Gao, Yuhe & Shi, Chengchun & Song, Rui, 2023. "Deep spectral Q-learning with application to mobile health," LSE Research Online Documents on Economics 119445, London School of Economics and Political Science, LSE Library.
    6. Zhou, Yunzhe & Qi, Zhengling & Shi, Chengchun & Li, Lexin, 2023. "Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach," LSE Research Online Documents on Economics 118233, London School of Economics and Political Science, LSE Library.
    7. Qiong Zhang & Amin Khademi & Yongjia Song, 2022. "Min-Max Optimal Design of Two-Armed Trials with Side Information," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 165-182, January.
    8. Shi, Chengchun & Zhang, Shengxing & Lu, Wenbin & Song, Rui, 2022. "Statistical inference of the value function for reinforcement learning in infinite-horizon settings," LSE Research Online Documents on Economics 110882, London School of Economics and Political Science, LSE Library.

    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. Muxuan Liang & Menggang Yu, 2023. "Relative contrast estimation and inference for treatment recommendation," Biometrics, The International Biometric Society, vol. 79(4), pages 2920-2932, December.
    2. Xin Chen & Rui Song & Jiajia Zhang & Swann Arp Adams & Liuquan Sun & Wenbin Lu, 2022. "On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime," Biometrics, The International Biometric Society, vol. 78(4), pages 1377-1389, December.
    3. Cai, Hengrui & Shi, Chengchun & Song, Rui & Lu, Wenbin, 2023. "Jump interval-learning for individualized decision making with continuous treatments," LSE Research Online Documents on Economics 118231, London School of Economics and Political Science, LSE Library.
    4. Baojiang Chen & Ao Yuan & Jing Qin, 2022. "Pool adjacent violators algorithm–assisted learning with application on estimating optimal individualized treatment regimes," Biometrics, The International Biometric Society, vol. 78(4), pages 1475-1488, December.
    5. 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.
    6. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    7. 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.
    8. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    9. Kushal S. Shah & Haoda Fu & Michael R. Kosorok, 2023. "Stabilized direct learning for efficient estimation of individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(4), pages 2843-2856, December.
    10. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
    11. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    12. Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
    13. 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.
    14. 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.
    15. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    16. 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.
    17. Xinyang Huang & Jin Xu, 2020. "Estimating individualized treatment rules with risk constraint," Biometrics, The International Biometric Society, vol. 76(4), pages 1310-1318, December.
    18. Hyung Park & Thaddeus Tarpey & Eva Petkova & R. Todd Ogden, 2024. "A high-dimensional single-index regression for interactions between treatment and covariates," Statistical Papers, Springer, vol. 65(7), pages 4025-4056, September.
    19. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "A sparse random projection-based test for overall qualitative treatment effects," LSE Research Online Documents on Economics 102107, London School of Economics and Political Science, LSE Library.
    20. Giorgos Bakoyannis, 2023. "Estimating optimal individualized treatment rules with multistate processes," Biometrics, The International Biometric Society, vol. 79(4), pages 2830-2842, December.

    More about this item

    Keywords

    heterogeneity; maximin projection learning; optimal treatment regime; quadratically constrained linear programming;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:ehl:lserod:102112. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.