  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.
