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  3. Robust Privacy-preserving Models For Cluster-level Confounding: Recognizing Disparities In Access To Transplantation.
  1. Home
  3. Robust Privacy-preserving Models For Cluster-level Confounding: Recognizing Disparities In Access To Transplantation.

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Robust Privacy-Preserving Models for Cluster-Level Confounding: Recognizing Disparities in Access to Transplantation.

Nicholas Hartman1,2, Kevin He1,2

  • 1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.

Statistics in Biosciences
|January 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a privacy-preserving method to accurately evaluate medical providers by adjusting for patient and regional factors. The novel approach enhances healthcare equity, particularly in transplant care.

Keywords:
ConfoundersCorrelated random effectsEmpirical nullProvider profiling

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Health Equity

Background:

  • Estimating medical provider treatment effects requires adjusting for patient and cluster-level confounding factors.
  • Existing risk-adjustment methods often fail to account for crucial cluster-level confounders and unobservable factors.
  • Limited patient data, unobserved confounders, and outlying clusters complicate provider evaluations.

Purpose of the Study:

  • To develop a privacy-preserving model and inference method for robust provider treatment effect estimation.
  • To adjust for observed cluster-level confounders and correct for unobserved confounding factors.
  • To improve equity in healthcare, specifically in transplant center evaluations.

Main Methods:

  • Proposed a privacy-preserving model and a novel Pseudo-Bayesian inference method.
  • Derived theoretical connections to the Correlated Random Effects model.
  • Applied methods to evaluate transplant centers, adjusting for geographic disparities and unobserved factors.

    • Main Results:

    • The proposed method offers robust assessment of provider treatment effects.
    • Demonstrated advantages in estimation stability, computational efficiency, and privacy preservation.
    • Successfully adjusted for observed geographic disparities and corrected for unobserved confounding in transplant care.

    Conclusions:

    • The novel Pseudo-Bayesian method provides a stable, efficient, and privacy-preserving approach to provider evaluation.
    • Addresses limitations of existing risk-adjustment methods by incorporating cluster-level and unobserved confounders.
    • Contributes to improving healthcare equity through more accurate provider performance assessment, exemplified by transplant center analysis.