Preserving data privacy when using multi-site data to estimate individualized treatment rules

TL;DR

This paper compares privacy-preserving methods for estimating individualized treatment rules in multi-site studies, finding distributed regression maintains performance better than data pooling, with applications to Warfarin dosing.

Contribution

It introduces and evaluates privacy-preserving approaches combining covariate microaggregation and distributed regression with dynamic weighted least squares for personalized treatment decision estimation.

Findings

Distributed regression maintains double robustness and reduces bias.

Data pooling can lead to bias due to loss of robustness.

Methods are demonstrated on Warfarin dosing data.

Abstract

Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formalize the individualization of treatment decisions that characterize personalized management plans. Numerous methods have been proposed to estimate individualized treatment rules that optimize expected patient outcomes, many of which have desirable properties such as robustness to model misspecification. However, while individual data are essential in this context, there may be concerns about data confidentiality, particularly in multi-centre studies where data are shared externally. To address this issue, we compared two approaches to privacy preservation: (i) data pooling, which is a covariate microaggregation technique and (ii) distributed regression. These approaches were combined with the doubly robust yet user-friendly method of dynamic weighted ordinary least squares to estimate individualized treatment rules. In simulations, we extensively evaluated the performance of the methods in estimating the parameters of the decision rule under different assumptions. The results demonstrate that double robustness is not maintained in data pooling setting and that this can result in bias, whereas the distributed regression provides good performance. We illustrate the methods via an analysis of optimal Warfarin dosing using data from the International Warfarin Consortium.