Likelihood-based Modeling of Covariate-Specific Time-Dependent ROC Curves

TL;DR

This paper introduces a covariate-adjusted, likelihood-based framework for modeling time-dependent ROC curves in longitudinal studies, improving biomarker prognostic accuracy assessment in heterogeneous patient populations.

Contribution

It develops the nonparanormal prognostic biomarker framework that models joint distributions to estimate covariate-specific ROC curves, addressing limitations of existing methods.

Findings

Serum neurofilament light's prognostic accuracy varies over time and with patient characteristics.

The framework captures heterogeneity in biomarker performance across covariates.

Application to ALS demonstrates improved risk stratification.

Abstract

Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it difficult to account for patient heterogeneity. In amyotrophic lateral sclerosis (ALS), factors such as age, site of onset and genetic status influence both survival and biomarker levels, yet their impact on the prognostic accuracy of biomarkers over time remains unclear. While time-dependent receiver operating characteristic methods have been developed to handle censored time-to-event outcomes, most do not adjust for covariates. To address this, we propose the nonparanormal prognostic biomarker framework, which models the joint distribution of the biomarker and event time while accounting for covariates. This allows estimation of covariate-specific time-dependent ROC curves and related summary measures. We apply the NPB framework to evaluate serum neurofilament light as a prognostic biomarker in ALS, showing that its accuracy varies over time and with patient characteristics. By capturing these covariate-specific effects, the NPB framework supports more targeted risk stratification and can potentially improve the design of clinical trials for new ALS treatments.