Biomarker assessment and combination with differential covariate effects and an unknown gold standard, with an application to Alzheimer’s disease

Abstract

The continued efforts to evaluate biomarkers’ predictive abilities and identify optimal biomarker combinations are often challenged by the absence of a gold standard, that is, the true disease status. Current methods that address this issue are mostly developed for binary or ordinal diagnostic tests, which do not fully utilize information provided by continuous biomarkers, or require strong parametric assumptions. Moreover, limited methods exist to allow for the inclusion of covariates—despite their crucial role in facilitating the accurate evaluation of biomarkers. In this paper, we proposed a latent profile approach to evaluating diagnostic accuracy of biomarkers without a gold standard. The method allows for flexible biomarker distributions and incorporation of previous knowledge about risk factors while simultaneously permitting researchers to model paticipants’ characteristics that putatively affect biomarker levels, and therefore provides information needed to develop more personalized diagnostic procedures. Additionally, the proposed method presents a potential strategy for biomarker combination when gold standard information is unavailable, as it derives a composite risk score for the underlying disease status. The method is applied to evaluate different cerebral spinal fluid (CSF) biomarkers for Alzheimer’s disease (AD) detection. The results show that CSF biomarkers hold significant potential for facilitating early AD detection and for continuous disease monitoring. Furthermore, they call attention to biomarker variability in subgroups and reexamination of CSF biomarker distributions. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.