Biomarker combinations for diagnosis and prognosis in multicenter studies: Principles and methods.

ABSTRACT: Many investigators are interested in combining biomarkers to predict a binary outcome or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misl

SUBMITTER: Meisner A 

PROVIDER: S-EPMC9835724 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature