Erica E. M. Moodie (Universit茅 McGill)
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TITRE / TITLE Estimating individualized treatment rules is challenging, as the treatment effect heterogeneity of interest often suffers from low power. This motivates the use of very large datasets such as those from multiple health systems or multicentre studies, which may raise concerns of data privacy. In this talk, I will introduce a statistical framework for of estimation individualized treatment rules and show how distributed regression can be used in combination with dynamic weighted regression to find an optimal individualized treatment rule whilst obscuring individual-level data. The robustness of this approach and its flexibility to address local treatment practices will be shown in simulation. The work is motivated by, and illustrated with, an analysis of the U.K.鈥檚 Clinical Practice Research Datalink on the treatment of depression. LIEU / PLACE |