Accounting for Competing Risks in the Assessment of Prognostic Biomarkers' Discriminative Accuracy.

The discriminative performance of biomarkers often changes over time and exhibits heterogeneity across subgroups defined by patient characteristics. Assessing how this performance varies with these factors is crucial for a comprehensive evaluation of biomarkers and to identify areas for improvement in sub-populations with poor performance. Additionally, the presence of competing risks complicates the assessment of discriminative performance. Ignoring competing risks can lead to misleading conclusions, as the biomarker's performance for the event of interest, such as disease onset, may be confounded by its performance for competing events, such as death. To address these challenges, we develop a regression model to assess the impact of covariates on the discriminative performance of biomarkers, characterized by the covariate-specific time-dependent Area-undercurve (AUC) for a specific cause. We construct a pseudo partial-likelihood for estimation and inference and establish the asymptotic properties of the proposed estimators. Through simulation studies, we demonstrate the finite sample performance of these estimators, and we apply the proposed method to data from the African American Study of Kidney Disease and Hypertension (AASK).