Impact of methodological assumptions and covariates on the cutoff estimation in ROC analysis

TL;DR

This paper investigates how methodological assumptions and covariates influence the estimation of optimal cutoffs in ROC analysis, emphasizing the need for covariate-specific thresholds and evaluating various estimation methods through simulations and real data.

Contribution

It introduces a covariate-based framework for more accurate cutoff estimation in ROC analysis, addressing gaps in existing methodologies and assessing their performance with simulations and ADNI data.

Findings

Covariate effects significantly alter optimal cutoff estimates.

Different ROC estimation methods vary in accuracy across scenarios.

Application to ADNI data identifies suitable biomarkers and thresholds.

Abstract

The Receiver Operating Characteristic (ROC) curve stands as a cornerstone in assessing the efficacy of biomarkers for disease diagnosis. Beyond merely evaluating performance, it provides with an optimal cutoff for biomarker values, crucial for disease categorization. While diverse methodologies exist for threshold estimation, less attention has been paid to integrating covariate impact into this process. Covariates can strongly impact diagnostic summaries, leading to variations across different covariate levels. Therefore, a tailored covariate-based framework is imperative for outlining covariate-specific optimal cutoffs. Moreover, recent investigations into cutoff estimators have overlooked the influence of ROC curve estimation methodologies. This study endeavors to bridge this gap by addressing the research void. Extensive simulation studies are conducted to scrutinize the performance of ROC curve estimation models in estimating different cutoffs in varying scenarios, encompassing diverse data-generating mechanisms and covariate effects. Additionally, leveraging the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the research assesses the performance of different biomarkers in diagnosing Alzheimer's disease and determines the suitable optimal cutoffs.