Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test.
Not until recently has much attention been given to deriving maximum likelihood methods for estimating the intercept and slope parameters from a binormal ROC curve that assesses the accuracy of a continuous diagnostic test. We propose two new methods for estimating these parameters. The first method uses the profile likelihood and a simple algorithm to produce fully efficient estimates. The second method is based on a pseudo-maximum likelihood that can easily accommodate adjusting for covariates that could affect the accuracy of the continuous test.
Cai
Tianxi
T
Department of Biostatistics, Harvard University, Boston, MA 02115, USA. tcai@hsph.harvard.edu
Moskowitz
Chaya S
CS
D000465:Algorithms; D018394:CA-125 Antigen / Q000097:blood; D018395:CA-19-9 Antigen / Q000097:blood; D003198:Computer Simulation; D003955:Diagnostic Tests, Routine / Q000379:methods / Q000592:standards*; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D011471:Prostatic Neoplasms / Q000175:diagnosis; D012372:ROC Curve
D018394:CA-125 Antigen; D018395:CA-19-9 Antigen