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Demystifying Optimal Dynamic Treatment Regimes

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  • Erica E. M. Moodie
  • Thomas S. Richardson
  • David A. Stephens

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  • 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.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:2:p:447-455
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00686.x
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    References listed on IDEAS

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    1. P. W. Lavori & R. Dawson, 2000. "A design for testing clinical strategies: biased adaptive within‐subject randomization," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 29-38.
    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.
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    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. 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.
    3. Rich Benjamin & Moodie Erica E. M. & A. Stephens David, 2016. "Influence Re-weighted G-Estimation," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 157-177, May.
    4. Luo, Yu & Graham, Daniel J. & McCoy, Emma J., 2023. "Semiparametric Bayesian doubly robust causal estimation," LSE Research Online Documents on Economics 117944, London School of Economics and Political Science, LSE Library.
    5. 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.
    6. 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.
    7. 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.
    8. Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2023. "Inference on Optimal Dynamic Policies via Softmax Approximation," Papers 2303.04416, arXiv.org, revised Dec 2023.
    9. 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.
    10. 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.
    11. 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.
    12. Tsai Kao-Tai & Peace Karl, 2013. "Analysis of Subgroup Data of Clinical Trials," Journal of Causal Inference, De Gruyter, vol. 1(2), pages 193-207, September.
    13. Sies Aniek & Van Mechelen Iven, 2017. "Comparing Four Methods for Estimating Tree-Based Treatment Regimes," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-20, May.

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