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Constructing dynamic treatment regimes over indefinite time horizons

Author

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  • Ashkan Ertefaie
  • Robert L Strawderman

Abstract

SUMMARYExisting methods for estimating optimal dynamic treatment regimes are limited to cases where a utility function is optimized over a fixed time period. We develop an estimation procedure for the optimal dynamic treatment regime over an indefinite time period and derive associated large-sample results. The proposed method can be used to estimate the optimal dynamic treatment regime in chronic disease settings. We illustrate this by simulating a dataset corresponding to a cohort of patients with diabetes that mimics the third wave of the National Health and Nutrition Examination Survey, and examining the performance of the proposed method in controlling the level of haemoglobin A1c.

Suggested Citation

  • Ashkan Ertefaie & Robert L Strawderman, 2018. "Constructing dynamic treatment regimes over indefinite time horizons," Biometrika, Biometrika Trust, vol. 105(4), pages 963-977.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:4:p:963-977.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy043
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
    4. 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.
    5. 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.
    6. Kim Kwangho & Kennedy Edward H. & Naimi Ashley I., 2021. "Incremental intervention effects in studies with dropout and many timepoints#," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 302-344, January.
    7. 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.

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