Privacy-Protecting Analytical Methods Using Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.

Privacy-Protecting Analytical Methods Using Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.
Am J Epidemiol. 2018 Dec 07;:
Authors: Li X, Fireman BH, Curtis JR, Arterburn DE, Fisher DP, Moyneur É, Gallagher M, Raebel MA, Nowell WB, Lagreid L, Toh S
Abstract
Distributed data networks enable large-scale epidemiologic studies but protecting privacy while adequately adjusting for a large number of covariates continues to pose methodological challenges. Using two empirical examples within a three-site distributed data network, we tested combinations of three aggregate-level data-sharing approaches (risk-set, summary-table, effect-estimate), four confounding adjustment methods (matching, stratification, inverse probability weighting, match weighting), and two summary scores (propensity score, disease risk score) for binary and time-to-event outcomes. We assessed the performance of these data-sharing and adjustment method combinations by comparing their results against the results from the corresponding pooled individual-level data analysis (reference). For both outcome types, the method combinations examined yielded identical or comparable results to the reference in most scenarios. Within each data-sharing approach, comparability between aggregate- and individual-level data analysis depended on adjustment method, e.g., risk-set data sharing with matched or stratified analysis of su...

Source: Am J Epidemiol - December 7, 2018 Category: Epidemiology Authors: Li X, Fireman BH, Curtis JR, Arterburn DE, Fisher DP, Moyneur É, Gallagher M, Raebel MA, Nowell WB, Lagreid L, Toh S Tags: Am J Epidemiol Source Type: research