Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies.

ABSTRACT: Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via ana

SUBMITTER: Han Y 

PROVIDER: S-EPMC10086614 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature