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Comparison of privacy-protecting analytic and data-sharing methods: A simulation study.

Kazuki Yoshida1,2, Susan Gruber3, Bruce H Fireman4

  • 1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Pharmacoepidemiology and Drug Safety
|July 20, 2018
PubMed
Summary

Privacy-preserving data sharing for analytics is feasible without individual-level data. Aggregate data analysis methods generally approximate pooled results, but caution is advised for meta-analysis in rare outcome or exposure scenarios.

Keywords:
distributed databasespharmacoepidemiologyprivacy-protecting methodspropensity score

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Data Privacy

Background:

  • Growing need for cross-source data utilization necessitates privacy-preserving methods.
  • Minimizing disclosure risk of sensitive information is crucial in distributed data networks.

Purpose of the Study:

  • To evaluate the impact of avoiding individual-level data sharing on analytic outcomes.
  • To compare aggregate data analysis methods against pooled individual-level data analysis.

Main Methods:

  • Simulation study with varying parameters (prevalence, incidence, site size, etc.).
  • Confounding adjustment using propensity scores or disease risk scores.
  • Comparison of risk-set, summary-table, and meta-analysis data with pooled individual data.

Main Results:

  • Aggregate data approaches generally approximated pooled analysis results.
  • Meta-analysis showed minor bias in scenarios with infrequent exposure and rare outcomes.
  • Standard error estimates varied in accuracy depending on the method and data characteristics.

Conclusions:

  • Valid analytic results can often be obtained without sharing individual-level data.
  • Careful consideration is needed for meta-analysis in specific low-frequency scenarios, especially with inverse probability of treatment weighting (IPTW).