IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v71y2015i3p636-644.html
   My bibliography  Save this article

Doubly‐robust dynamic treatment regimen estimation via weighted least squares

Author

Listed:
  • Michael P. Wallace
  • Erica E. M. Moodie

Abstract

Personalized medicine is a rapidly expanding area of health research wherein patient level information is used to inform their treatment. Dynamic treatment regimens (DTRs) are a means of formalizing the sequence of treatment decisions that characterize personalized management plans. Identifying the DTR which optimizes expected patient outcome is of obvious interest and numerous methods have been proposed for this purpose. We present a new approach which builds on two established methods: Q‐learning and G‐estimation, offering the doubly robust property of the latter but with ease of implementation much more akin to the former. We outline the underlying theory, provide simulation studies that demonstrate the double‐robustness and efficiency properties of our approach, and illustrate its use on data from the Promotion of Breastfeeding Intervention Trial.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:636-644
    DOI: 10.1111/biom.12306
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12306
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12306?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Yufan Zhao & Donglin Zeng & Mark A. Socinski & Michael R. Kosorok, 2011. "Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer," Biometrics, The International Biometric Society, vol. 67(4), pages 1422-1433, December.
    4. 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.
    5. Robin Henderson & Phil Ansell & Deyadeen Alshibani, 2010. "Regret-Regression for Optimal Dynamic Treatment Regimes," Biometrics, The International Biometric Society, vol. 66(4), pages 1192-1201, December.
    6. 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.
    7. 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.
    8. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2013. "Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions," Biometrika, Biometrika Trust, vol. 100(3), pages 681-694.
    9. 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.
    10. Lu Wang & Andrea Rotnitzky & Xihong Lin & Randall E. Millikan & Peter F. Thall, 2012. "Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 493-508, June.
    11. Rich Benjamin & Moodie Erica E. M. & Stephens David A & Platt Robert W, 2010. "Model Checking with Residuals for g-estimation of Optimal Dynamic Treatment Regimes," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-24, March.
    12. Moodie, Erica E. M. & Platt, Robert W. & Kramer, Michael S., 2009. "Estimating Response-Maximized Decision Rules With Applications to Breastfeeding," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 155-165.
    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. 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. Lingyun Lyu & Yu Cheng & Abdus S. Wahed, 2023. "Imputation‐based Q‐learning for optimizing dynamic treatment regimes with right‐censored survival outcome," Biometrics, The International Biometric Society, vol. 79(4), pages 3676-3689, December.
    3. Shuo Sun & Erica E. M. Moodie & Johanna G. Nešlehová, 2021. "Causal inference for quantile treatment effects," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    4. Wallace, Michael P. & Moodie, Erica E. M. & Stephens, David A., 2017. "Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i02).
    5. Peng Ding, 2022. "Peng Ding’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 691-693, July.
    6. Guanhua Chen & Donglin Zeng & Michael R. Kosorok, 2016. "Personalized Dose Finding Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1509-1521, October.
    7. 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.
    8. Jiacheng Wu & Nina Galanter & Susan M. Shortreed & Erica E.M. Moodie, 2022. "Ranking tailoring variables for constructing individualized treatment rules: An application to schizophrenia," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 309-330, March.
    9. 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.
    10. 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.
    11. Erica E. M. Moodie & Janie Coulombe & Coraline Danieli & Christel Renoux & Susan M. Shortreed, 2022. "Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 512-542, July.
    12. 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.

    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. 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.
    2. 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.
    3. Kristin A. Linn & Eric B. Laber & Leonard A. Stefanski, 2017. "Interactive -Learning for Quantiles," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 638-649, April.
    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. 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.
    6. 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.
    7. Wallace, Michael P. & Moodie, Erica E. M. & Stephens, David A., 2017. "Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i02).
    8. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
    9. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    10. Hyung Park & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2021. "A constrained single‐index regression for estimating interactions between a treatment and covariates," Biometrics, The International Biometric Society, vol. 77(2), pages 506-518, June.
    11. 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.
    12. 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.
    13. James Y. Dai & C. Jason Liang & Michael LeBlanc & Ross L. Prentice & Holly Janes, 2018. "Case†only approach to identifying markers predicting treatment effects on the relative risk scale," Biometrics, The International Biometric Society, vol. 74(2), pages 753-763, June.
    14. 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.
    15. 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.
    16. 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.
    17. Giorgos Bakoyannis, 2023. "Estimating optimal individualized treatment rules with multistate processes," Biometrics, The International Biometric Society, vol. 79(4), pages 2830-2842, December.
    18. 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.
    19. 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.
    20. 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.

    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:bla:biomet:v:71:y:2015:i:3:p:636-644. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.