Preserving privacy in estimating individualized treatment rules from distributed data collection sites

Abstract: Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform care via individualized treatment rules (ITR). Identifying the ITR which optimizes expected patient outcome is of obvious interest, and has to date been done exclusively using individual-level data. However, estimating ITRs require large amounts of data and may necessitate multi-centre collaborations. This may raise concerns about data privacy. In this talk, I will introduce ITRs and a straightforward, doubly-robust estimation method and discuss approaches to preserving privacy while producing unbiased estimates of rules that tailor treatment to individual characteristics. The preferred approach is illustrated via analyses of warfarin dosing and front-line treatment of depression. Bio: Erica E. M. Moodie is a Professor of Biostatistics and a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine at McGill University. Her main research interests are in causal inference and longitudinal data with a focus on precision medicine. She is the 2020 recipient of the CRM-SSC Prize in Statistics and an Elected Member of the International Statistical Institute. She holds a chercheur de merite career award from the Fonds de recherche du Quebec-Sante.