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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Protecting count queries in study design.

Staal A Vinterbo1, Anand D Sarwate, Aziz A Boxwala

  • 1Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California 92093-0728, USA. sav@ucsd.edu

Journal of the American Medical Informatics Association : JAMIA
|April 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel system for perturbing patient counts in clinical research data warehouses, enhancing privacy while allowing users to tailor data utility. Researchers can now control privacy levels and improve cohort estimate accuracy.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Health Informatics
  • Data Privacy
  • Clinical Research

Background:

  • Clinical research institutions use data warehouses for patient counts.
  • Current privacy methods perturb counts, reducing data utility.
  • A need exists for quantifiable privacy with user-controlled utility.

Purpose of the Study:

  • To extend query answer systems with quantifiable privacy.
  • To enable users to tailor data perturbations for maximum usefulness.
  • To balance privacy guarantees with research data utility.

Main Methods:

  • A perturbation mechanism allowing user control over scale and direction.
  • Translating true counts, user preferences, and privacy levels into a probability distribution.
  • Developing an open-source, web-enabled tool for parameter investigation.

Main Results:

  • Users can influence perturbation scale and direction for accurate cohort estimates.
  • Strong differential privacy is guaranteed with a unified accounting system.
  • Demonstrated interaction between system parameters, privacy levels, and user preferences.

Conclusions:

  • Quantifiable privacy enables administrators to set privacy budgets and monitor expenditure.
  • Users can control the trade-off between privacy and data utility.
  • The system offers novel approaches to balancing privacy and utility in study design.