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Binary isotonic regression procedures, with application to cancer biomarkers


  • Debashis Ghosh

    (University of Michigan)

  • Moulinath Banerjee

    (University of Michigan)

  • Pinaki Biswas

    (Univeristy of Michigan)


There is a lot of interest in the development and characterization of new biomarkers for screening large populations for disease. In much of the literature on diagnostic testing, increased levels of a biomarker correlate with increased disease risk. However, parametric forms are typically used to associate these quantities. In this article, we specify a monotonic relationship between biomarker levels with disease risk. This leads to consideration of a nonparametric regression model for a single biomarker. Estimation results using isotonic regression-type estimators and asymptotic results are given. We also discuss confidence set estimation in this setting and propose three procedures for computing confidence intervals. Methods for estimating the receiver operating characteristic (ROC) curve are also described. The finite-sample properties of the proposed methods are assessed using simulation studies and applied to data from a pancreatic cancer biomarker study.

Suggested Citation

  • Debashis Ghosh & Moulinath Banerjee & Pinaki Biswas, 2004. "Binary isotonic regression procedures, with application to cancer biomarkers," The University of Michigan Department of Biostatistics Working Paper Series 1037, Berkeley Electronic Press.
  • Handle: RePEc:bep:mchbio:1037 Note:

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    References listed on IDEAS

    1. Delgado, Miguel A. & Rodriguez-Poo, Juan M. & Wolf, Michael, 2001. "Subsampling inference in cube root asymptotics with an application to Manski's maximum score estimator," Economics Letters, Elsevier, vol. 73(2), pages 241-250, November.
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