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Am J Epidemiol. 2019 Apr 1;188(4):709-723. doi: 10.1093/aje/kwy265.

Validity of Privacy-Protecting Analytical Methods That Use Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.

Author information

1
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
2
Division of Research, Kaiser Permanente Northern California, Oakland, California.
3
Division of Clinical Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
4
Kaiser Permanente Washington Health Research Institute, Seattle, Washington.
5
The Permanente Medical Group, Kaiser Permanente Northern California, Oakland, California.
6
StatLog Econometrics Inc., Montreal, Quebec, Canada.
7
Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado.
8
CreakyJoints, Global Healthy Living Foundation, Upper Nyack, New York.
9
Limeade, Bellevue, Washington.

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 2 empirical examples within a 3-site distributed data network, we tested combinations of 3 aggregate-level data-sharing approaches (risk-set, summary-table, and effect-estimate), 4 confounding adjustment methods (matching, stratification, inverse probability weighting, and matching weighting), and 2 summary scores (propensity score and disease risk score) for binary and time-to-event outcomes. We assessed the performance of combinations of these data-sharing and adjustment methods by comparing their results with results from the corresponding pooled individual-level data analysis (reference analysis). For both types of outcomes, the method combinations examined yielded results identical or comparable to the reference results in most scenarios. Within each data-sharing approach, comparability between aggregate- and individual-level data analysis depended on adjustment method; for example, risk-set data-sharing with matched or stratified analysis of summary scores produced identical results, while weighted analysis showed some discrepancies. Across the adjustment methods examined, risk-set data-sharing generally performed better, while summary-table and effect-estimate data-sharing more often produced discrepancies in settings with rare outcomes and small sample sizes. Valid multivariable-adjusted analysis can be performed in distributed data networks without sharing of individual-level data.

KEYWORDS:

confounding control; data-sharing; disease risk score; distributed data networks; meta-analysis; multicenter studies; privacy protection; propensity score

PMID:
30535131
PMCID:
PMC6438804
[Available on 2020-04-01]
DOI:
10.1093/aje/kwy265

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