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Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes

Author

Listed:
  • Erica E. M. Moodie

    (McGill University)

  • Janie Coulombe

    (McGill University)

  • Coraline Danieli

    (McGill University)

  • Christel Renoux

    (McGill University
    Jewish General Hospital
    McGill University)

  • Susan M. Shortreed

    (Kaiser Permanente Washington Health Research Institute
    University of Washington)

Abstract

Estimating individualized treatment rules—particularly in the context of right-censored outcomes—is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom’s Clinical Practice Research Datalink.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:3:d:10.1007_s10985-022-09554-8
    DOI: 10.1007/s10985-022-09554-8
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    References listed on IDEAS

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    1. Chakraborty, B., 2011. "Dynamic treatment regimes for managing chronic health conditions: A statistical perspective," American Journal of Public Health, American Public Health Association, vol. 101(1), pages 40-45.
    2. 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.
    3. Fan Li & Kari Lock Morgan & Alan M. Zaslavsky, 2018. "Balancing Covariates via Propensity Score Weighting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 390-400, January.
    4. Juliana Schulz & Erica E. M. Moodie, 2021. "Doubly Robust Estimation of Optimal Dosing Strategies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 256-268, March.
    5. 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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