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Two transformation models for estimating an ROC curve derived from continuous data

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Kelly H. Zou, W. J. Hall
Abstract

A receiver operating characteristic (ROC) curve is a plot of two survival functions, derived separately from the diseased and healthy samples. A special feature is that the ROC curve is invariant to any monotone transformation of the measurement scale. We propose and analyse semiparametric and parametric transformation models for this two-sample problem. Following an unspecified or specified monotone transformation, we assume that the healthy and diseased measurements have two normal distributions with different means and variances. Maximum likelihood algorithms for estimating ROC curve parameters are developed. The proposed methods are illustrated on the marker CA125 in the diagnosis of gastric cancer.

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Article provided by Taylor and Francis Journals in its journal Journal of Applied Statistics.

Volume (Year): 27 (2000)
Issue (Month): 5 (July)
Pages: 621-631
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Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:621-631

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  1. Peter G. Hall & Rob J. Hyndman & Yanan Fan, 2003. "Non Parametric Confidence Intervals for Receiver Operating Characteristic Curves," Monash Econometrics and Business Statistics Working Papers 12/03, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Kelly H. Zou & W. J. Hall, 2002. "Semiparametric and parametric transformation models for comparing diagnostic markers with paired design," Journal of Applied Statistics, Taylor and Francis Journals, vol. 29(6), pages 803-816, August. [Downloadable!] (restricted)
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