Extension of Dynamic Network Biomarker using the propensity score method: Simulation of causal effects on variance and correlation coefficient

TL;DR

This paper extends the Dynamic Network Biomarker method by integrating propensity score matching to address confounding bias in clinical biomarker data, demonstrating improved bias reduction through simulation.

Contribution

It introduces a novel approach combining DNB with propensity score matching for causal inference in clinical biomarker analysis.

Findings

PSM reduces bias in DNB-based group comparisons

Simulation shows improved accuracy of variance and correlation estimates

Method enhances causal interpretation of clinical biomarker signals

Abstract

In clinical biomarker studies, the Dynamic Network Biomarker (DNB) is sometimes used. DNB is a composite variable derived from the variance and the Pearson correlation coefficient of biological signals. When applying DNB to clinical data, it is important to account for confounding bias. However, little attention has been paid to statistical causal inference methods for variance and correlation coefficients. This study evaluates confounding adjustment using propensity score matching (PSM) through Monte Carlo simulations. Our results support the use of PSM to reduce bias and improve group comparisons when DNB is applied to clinical data.