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

TL;DR

This paper introduces a privacy-preserving, robust statistical model for estimating treatment effects at the cluster level, effectively adjusting for both observed and unobserved confounders using only summary data, with applications in healthcare disparities.

Contribution

It presents a novel model that relies solely on publicly-available summaries, handles outliers, and includes a Pseudo-Bayesian inference method for unobservable confounders, advancing privacy-preserving cluster analysis.

Findings

Simulations demonstrate robustness and accuracy of estimates.

Proposed inference outperforms existing methods in frequentist properties.

Application reveals disparities in transplant center access.

Abstract

In applications where the study data are collected within cluster units (e.g., patients within transplant centers), it is often of interest to estimate and perform inference on the treatment effects of the cluster units. However, it is well-established that cluster-level confounding variables can bias these assessments, and many of these confounding factors may be unobservable. In healthcare settings, data sharing restrictions often make it impossible to directly fit conventional risk-adjustment models on patient-level data, and existing privacy-preserving approaches cannot adequately adjust for both observed and unobserved cluster-level confounding factors. In this paper, we propose a privacy-preserving model for cluster-level confounding that only depends on publicly-available summary statistics, can be fit using a single optimization routine, and is robust to outlying cluster unit effects. In addition, we develop a Pseudo-Bayesian inference procedure that accounts for the estimated cluster-level confounding effects and corrects for the impact of unobservable factors. Simulations show that our estimates are robust and accurate, and the proposed inference approach has better Frequentist properties than existing methods. Motivated by efforts to improve equity in transplant care, we apply these methods to evaluate transplant centers while adjusting for observed geographic disparities in donor organ availability and unobservable confounders.