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Biostatistics 2004 5(4):573-586; doi:10.1093/biostatistics/kxh009
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Biostatistics Vol. 5 No. 4 © Oxford University Press 2004; all rights reserved.

Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test

Tianxi Cai*

Department of Biostatistics, Harvard University, Boston, MA 02115, USA
tcai{at}hsph.harvard.edu

Chaya S. Moskowitz

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA

* To whom correspondence should be addressed.

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.

Keywords: Classification; Diagnostic accuracy; Maximum likelihood; Screening; Semi-parametric transformation model


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