A model free approach to combining biomarkers.
For most diseases, single biomarkers do not have adequate sensitivity or specificity for practical purposes. We present an approach to combine several biomarkers into a composite marker score without assuming a model for the distribution of the predictors. Using sufficient dimension reduction techniques, we replace the original markers with a lower-dimensional version, obtained through linear transformations of markers that contain sufficient information for regression of the predictors on the outcome. We combine the linear transformations using their asymptotic properties into a scalar diagnostic score via the likelihood ratio statistic. The performance of this score is assessed by the area under the receiver-operator characteristics curve (ROC), a popular summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. An asymptotic chi-squared test for assessing individual biomarker contribution to the diagnostic score is also derived.
Pfeiffer
Ruth M
RM
Biostatistics Branch, National Cancer Institute, 6120 Executive Blvd, EPS/8030, Bethesda, MD 20892, USA. pfeiffer@mail.nih.gov
Bur
Efstathia
E
D000437:Alcoholism / Q000097:blood / Q000175:diagnosis / Q000652:urine; D000465:Algorithms; D015415:Biomarkers / Q000032:analysis* / Q000097:blood / Q000652:urine; D016009:Chi-Square Distribution; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
D015415:Biomarkers