A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification.

ABSTRACT:

Background

Combining multiple biomarkers into a single diagnostic score can improve disease classification. However, traditional methods such as logistic regression and linear discriminant analysis depend on restrictive distributional assumptions, which can limit their effectiveness when dealing with complex or heterogeneous datasets. To address these limitations, more practical methodological alternatives are required.

Methods

This study investigates the utility of two sufficient dimension reduction (SDR) methods, Minimum Average Variance Estimation (MAVE) and the Outer Product of Gradients (OPG), for constructing composite biomarker scores in binary diagnostic classification. A comprehensive simulation study was conducted under four data-generating scenarios, varying in sa

SUBMITTER: Ozen H 

PROVIDER: S-EPMC12870225 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature