Multivariable confounding adjustment in distributed data networks without sharing of patient-level data.

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PURPOSE

It is increasingly necessary to analyze data from multiple sources when conducting public health safety surveillance or comparative effectiveness research. However, security, privacy, proprietary, and legal concerns often reduce data holders' willingness to share highly granular information. We describe and compare two approaches that do not require sharing of patient-level information to adjust for confounding in multi-site studies.

METHODS

We estimated the risks of angioedema associated with angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and aliskiren in comparison with beta-blockers within Mini-Sentinel, which has created a distributed data system of 18 health plans. To obtain the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), we performed (i) a propensity score-stratified case-centered logistic regression analysis, a method identical to a stratified Cox regression analysis but needing only aggregated risk set data, and (ii) an inverse variance-weighted meta-analysis, which requires only the site-specific HR and variance. We also performed simulations to further compare the two methods.

RESULTS

Compared with beta-blockers, the adjusted HR was 3.04 (95% CI: 2.81, 3.27) for ACEIs, 1.16 (1.00, 1.34) for ARBs, and 2.85 (1.34, 6.04) for aliskiren in the case-centered analysis. The corresponding HRs were 2.98 (2.76, 3.21), 1.15 (1.00, 1.33), and 2.86 (1.35, 6.04) in the meta-analysis. Simulations suggested that the two methods may produce different results under certain analytic scenarios.

CONCLUSION

The case-centered analysis and the meta-analysis produced similar results without the need to share patient-level data across sites in our empirical study, but may provide different results in other study settings.

Investigators
Abbreviation
Pharmacoepidemiol Drug Saf
Publication Date
2013-07-23
Volume
22
Issue
11
Page Numbers
1171-7
Pubmed ID
23878013
Medium
Print-Electronic
Full Title
Multivariable confounding adjustment in distributed data networks without sharing of patient-level data.
Authors
Toh S, Reichman ME, Houstoun M, Ding X, Fireman BH, Gravel E, Levenson M, Li L, Moyneur E, Shoaibi A, Zornberg G, Hennessy S