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When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data

Author

Listed:
  • Cain Lauren E.

    (Harvard School of Public Health)

  • Robins James M.

    (Harvard School of Public Health)

  • Lanoy Emilie

    (INSERM U943)

  • Logan Roger

    (Harvard School of Public Health)

  • Costagliola Dominique

    (INSERM U943 and Université Pierre et Marie Curie)

  • Hernán Miguel A.

    (Harvard School of Public Health and Harvard-MIT Division of Health Sciences and Technology)

Abstract

Dynamic treatment regimes are the type of regime most commonly used in clinical practice. For example, physicians may initiate combined antiretroviral therapy the first time an individual's recorded CD4 cell count drops below either 500 cells/mm3 or 350 cells/mm3. This paper describes an approach for using observational data to emulate randomized clinical trials that compare dynamic regimes of the form "initiate treatment within a certain time period of some time-varying covariate first crossing a particular threshold." We applied this method to data from the French Hospital database on HIV (FHDH-ANRS CO4), an observational study of HIV-infected patients, in order to compare dynamic regimes of the form "initiate treatment within m months after the recorded CD4 cell count first drops below x cells/mm3" where x takes values from 200 to 500 in increments of 10 and m takes values 0 or 3. We describe the method in the context of this example and discuss some complications that arise in emulating a randomized experiment using observational data.

Suggested Citation

  • Cain Lauren E. & Robins James M. & Lanoy Emilie & Logan Roger & Costagliola Dominique & Hernán Miguel A., 2010. "When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-26, April.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:18
    DOI: 10.2202/1557-4679.1212
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    References listed on IDEAS

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    1. van der Laan Mark J. & Petersen Maya L, 2007. "Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-55, March.
    2. Orellana Liliana & Rotnitzky Andrea & Robins James M., 2010. "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-19, March.
    3. Orellana Liliana & Rotnitzky Andrea & Robins James M., 2010. "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-49, March.
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    Cited by:

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    2. Jincheng Shen & Lu Wang & Jeremy M. G. Taylor, 2017. "Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models," Biometrics, The International Biometric Society, vol. 73(2), pages 635-645, June.
    3. Yasuhiro Hagiwara & Tomohiro Shinozaki & Hirofumi Mukai & Yutaka Matsuyama, 2021. "Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two‐stage stochastic dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 77(2), pages 702-714, June.
    4. 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.
    5. Jacqueline A. Mauro & Edward H. Kennedy & Daniel Nagin, 2020. "Instrumental variable methods using dynamic interventions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1523-1551, October.
    6. Grace R. Lyden & David M. Vock & Erika S. Helgeson & Erik B. Finger & Arthur J. Matas & Jon J. Snyder, 2023. "Transportability of causal inference under random dynamic treatment regimes for kidney–pancreas transplantation," Biometrics, The International Biometric Society, vol. 79(4), pages 3165-3178, December.
    7. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    8. Lan Wen & Jessica G. Young & James M. Robins & Miguel A. Hernán, 2021. "Parametric g‐formula implementations for causal survival analyses," Biometrics, The International Biometric Society, vol. 77(2), pages 740-753, June.
    9. Noémi Kreif & Oleg Sofrygin & Julie A. Schmittdiel & Alyce S. Adams & Richard W. Grant & Zheng Zhu & Mark J. van der Laan & Romain Neugebauer, 2021. "Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies," Biometrics, The International Biometric Society, vol. 77(1), pages 329-342, March.
    10. Iván Díaz Muñoz & Mark van der Laan, 2012. "Population Intervention Causal Effects Based on Stochastic Interventions," Biometrics, The International Biometric Society, vol. 68(2), pages 541-549, June.
    11. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

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