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Privacy-preserving analytic methods for multisite comparative effectiveness and patient-centered outcomes research.

Sengwee Toh1, Susan Shetterly, John D Powers

  • 1*Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA †Institute for Health Research, Kaiser Permanente Colorado, Denver, CO ‡Group Health Research Institute, Seattle, WA.

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Summary-level data analysis methods, including propensity score-stratified analysis and meta-analysis, yield results comparable to individual-level data analysis in multisite studies, enhancing privacy and practicality.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Multisite studies often require minimizing individual-level data sharing due to privacy and practical concerns.
  • However, individual-level data is typically needed for rigorous statistical analysis in such studies.

Purpose of the Study:

  • To empirically compare three analytic methods using only summary-level information for multisite studies.
  • These methods aim to replicate analyses traditionally requiring individual-level data.

Main Methods:

  • Analysis of a 7-site bariatric surgery outcomes study (Scalable Partnering Network).
  • Comparison of adjustable gastric banding vs. Roux-en-y gastric bypass rehospitalization risk.
  • Methods included propensity score-stratified analysis, case-centered analysis, and meta-analysis, compared against pooled individual-level data analysis.

Main Results:

  • The individual-level data analysis showed an adjusted hazard ratio of 0.71 (95% CI, 0.59-0.84).
  • Propensity score-stratified analysis yielded 0.70 (0.59-0.83), case-centered analysis yielded 0.71 (0.59-0.84), and meta-analysis yielded 0.71 (0.60-0.84).
  • Results from summary-level methods were identical or highly comparable to the individual-level analysis.

Conclusions:

  • Propensity score-stratified analysis, case-centered analysis, and meta-analysis are viable alternatives for multisite studies.
  • These methods enable rigorous statistical analysis without sharing individual-level patient data.
  • They offer practical solutions when data sharing is not feasible or preferred.