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Privacy-protecting multivariable-adjusted distributed regression analysis for multi-center pediatric study.

Sengwee Toh1, Sheryl L Rifas-Shiman2, Pi-I D Lin2

  • 1Therapeutics Research and Infectious Disease Epidemiology Group, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA. darren_toh@harvardpilgrim.org.

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|October 3, 2019
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Summary
This summary is machine-generated.

Distributed regression, a privacy-preserving method, was validated in a large multi-center pediatric study. This approach enables multi-center research without sharing sensitive individual-level data, crucial for vulnerable populations.

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Area of Science:

  • Pediatric Health Research
  • Biostatistics
  • Data Privacy

Background:

  • Privacy-preserving analytic methods are vital for vulnerable populations like children.
  • Distributed regression has not been previously tested in multi-center pediatric studies.

Purpose of the Study:

  • To assess the feasibility and validity of distributed linear regression in a multi-center pediatric study.
  • To compare distributed regression with conventional pooled individual-level data analysis.

Main Methods:

  • Utilized electronic health data from 34 healthcare institutions (PCORnet).
  • Fitted 12 multivariable-adjusted linear regression models assessing antibiotic use and BMI z-score.
  • Compared results from pooled individual-level data analysis and distributed regression using summary-level data.

Main Results:

  • Distributed linear regression and pooled individual-level analyses yielded nearly identical parameter estimates and standard errors.
  • The maximum difference in parameter estimates or standard errors was extremely small (4.4833 × 10⁻¹⁰).

Conclusions:

  • Empirically demonstrated the feasibility and validity of distributed linear regression in a large multi-center pediatric study.
  • This privacy-preserving method can facilitate multi-center pediatric research where data sharing is difficult.