A Novel Two-stage Deming Regression Framework with Applications to Association Analysis between Clinical Risks

TL;DR

This paper introduces a two-stage Deming regression framework designed to accurately analyze associations between clinical risks with measurement errors, demonstrated through application to atrial fibrillation patient data for personalized treatment insights.

Contribution

The paper presents a novel two-stage Deming regression method that effectively accounts for uncertainties in both variables, improving analysis of clinical risk associations.

Findings

Successfully applied to AF patient data to explore stroke and bleeding risk relationships.

Enhanced accuracy in risk association analysis with multiple sources of uncertainty.

Supports personalized treatment decisions based on robust statistical modeling.

Abstract

In healthcare, clinical risks are crucial for treatment decisions, yet the analysis of their associations is often overlooked. This gap is particularly significant when balancing risks that are weighed against each other, as in the case of atrial fibrillation (AF) patients facing stroke and bleeding risks with anticoagulant medication. While traditional regression models are ill-suited for this task due to standard errors in risk estimation, a novel two-stage Deming regression framework is proposed to address this issue, offering a more accurate tool for analyzing associations between variables observed with errors of known or estimated variances. The first stage is to obtain the variable values with variances of errors either by estimation or observation, followed by the second stage that fits a Deming regression model potentially subject to a transformation. The second stage accounts for the uncertainties associated with both independent and response variables, including known or estimated variances and additional unknown variances from the model. The complexity arising from different scenarios of uncertainty is handled by existing and advanced variations of Deming regression models. An important practical application is to support personalized treatment recommendations based on clinical risk associations that were identified by the proposed framework. The model's effectiveness is demonstrated by applying it to a real-world dataset of AF-diagnosed patients to explore the relationship between stroke and bleeding risks, providing crucial guidance for making informed decisions regarding anticoagulant medication. Furthermore, the model's versatility in addressing data containing multiple sources of uncertainty such as privacy-protected data suggests promising avenues for future research in regression analysis.