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

Erica E. M. Moodie (), Janie Coulombe, Coraline Danieli, Christel Renoux and Susan M. Shortreed
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Erica E. M. Moodie: McGill University
Janie Coulombe: McGill University
Coraline Danieli: McGill University
Christel Renoux: McGill University
Susan M. Shortreed: Kaiser Permanente Washington Health Research Institute

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 3, No 7, 512-542

Abstract: 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.

Keywords: Data aggregation; Distributed regression; Dynamic weighted survival modelling; Effect modification; Precision medicine; Selective serotonin reuptake inhibitors; 92B15; 62P10 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09554-8

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