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Privacy-Protecting Analytical Methods Permit Multivariable-Adjusted Analysis Using Only Aggregate-Level Data in Multi-Center Distributed Data Networks: Results from Empirical Analyses.

By November 2, 2021December 9th, 2021Research Registry

Protecting privacy while adequately adjusting for a large number of covariates poses methodological challenges for distributed data networks that can enable large-scale epidemiologic studies. Using 2 empirical examples, Li et al. determined that when used in conjunction with confounder summary scores, several combinations of data-sharing approaches and confounding adjustment methods allow researchers to perform multivariable-adjusted analysis using only aggregate-level information from participating sites and produce results identical to or comparable to those from pooled individual-level data analysis which help to protect privacy.

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