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

TL;DR

This paper presents a privacy-preserving distributed method combining regression and survival modeling to estimate optimal individualized treatment rules for depression, effectively handling small effect sizes and data privacy concerns.

Contribution

It introduces a novel distributed regression approach integrated with dynamic weighted survival modeling for privacy-preserving treatment rule estimation.

Findings

The method effectively estimates treatment rules in simulated data.

DWSurv maintains double robustness in distributed settings.

Application to UK depression data demonstrates practical utility.

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.