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Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme

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  • Bibhas Chakraborty
  • Eric B. Laber
  • Yingqi Zhao

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  • 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.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:3:p:714-723
    DOI: 10.1111/biom.12052
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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. Laber, Eric B. & Murphy, Susan A., 2011. "Adaptive Confidence Intervals for the Test Error in Classification," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 904-913.
    3. 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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    Citations

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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ying Kuen Cheung & Bibhas Chakraborty & Karina W. Davidson, 2015. "Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program," Biometrics, The International Biometric Society, vol. 71(2), pages 450-459, June.
    8. Linn, Kristin A. & Laber, Eric B. & Stefanski, Leonard A., 2015. "iqLearn: Interactive Q-Learning in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i01).
    9. Yingchao Zhong & Chang Wang & Lu Wang, 2021. "Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare," Stats, MDPI, vol. 4(4), pages 1-17, September.
    10. Xinru WANG & Nina DELIU & NARITA Yusuke & Bibhas CHAKRABORTY, 2023. "SMART-EXAM: Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials," Discussion papers 23081, Research Institute of Economy, Trade and Industry (RIETI).
    11. 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.
    12. Zeyu Bian & Erica E. M. Moodie & Susan M. Shortreed & Sahir Bhatnagar, 2023. "Variable selection in regression‐based estimation of dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 79(2), pages 988-999, June.
    13. Nick Huntington-Klein & James Cowan & Dan Goldhaber, 2017. "Selection into Online Community College Courses and Their Effects on Persistence," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(3), pages 244-269, May.
    14. Yu Deng & Donglin Zeng & Jinying Zhao & Jianwen Cai, 2017. "Proportional hazards model with a change point for clustered event data," Biometrics, The International Biometric Society, vol. 73(3), pages 835-845, September.
    15. Subhankar Chattopadhyay & Raju Maiti & Samarjit Das & Atanu Biswas, 2022. "Change‐point analysis through integer‐valued autoregressive process with application to some COVID‐19 data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 4-34, February.

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