Privacy-Preserving Estimation of an Optimal Individualized Depression Treatment

• Activity Number: 37 - Novel Semiparametric Methods for Causal Inference
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: SSC (Statistical Society of Canada)
• Author(s): Erica EM Moodie* and Janie EM Coulombe and Susan M Shortreed and Christel Renoux
• Companies: McGill University and McGill University and Kaiser Permanente Washington Health Research Institute and Lady Davis Institute
• Keywords: Precision medicine; Adaptive treatment strategies; Dynamic treatment regimes; Individualized treatment rules; Privacy

Abstract

Estimating individualized treatment rules, particularly in the context of right-censored outcomes, faces a significant challenge in that the treatment effect heterogeneity of interest is often difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centers, 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 the U.K.’s Clinical Practice Research Datalink.