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DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups

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  • Luo, Jingqin
  • Xiong, Chengjie

Abstract

Medical researchers endeavor to identify potentially useful biomarkers to develop markerbased screening assays for disease diagnosis and prevention. Useful summary measures which properly evaluate the discriminative ability of diagnostic markers are critical for this purpose. Literature and existing software, for example, R packages nicely cover summary measures for diagnostic markers used for the binary case (e.g., healthy vs. diseased). An intermediate population at an early disease stage usually exists between the healthy and the fully diseased population in many disease processes. Supporting utilities for threegroup diagnostic tests are highly desired and important for identifying patients at the early disease stage for timely treatments. However, application packages which provide summary measures for three ordinal groups are currently lacking. This paper focuses on two summary measures of diagnostic accuracy—volume under the receiver operating characteristic surface and the extended Youden index, with three diagnostic groups. We provide the R package DiagTest3Grp to estimate, under both parametric and nonparametric assumptions, the two summary measures and the associated variances, as well as the optimal cut-points for disease diagnosis. An omnibus test for multiple markers and a Wald test for two markers, on independent or paired samples, are incorporated to compare diagnostic accuracy across biomarkers. Sample size calculation under the normality assumption can be performed in the R package to design future diagnostic studies. A real world application evaluating the diagnostic accuracy of neuropsychological markers for Alzheimer’s disease is used to guide readers through step-by-step implementation of DiagTest3Grp to demonstrate its utility.

Suggested Citation

  • Luo, Jingqin & Xiong, Chengjie, 2012. "DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i03).
    • Handle: RePEc:jss:jstsof:v:051:i03
    DOI: http://hdl.handle.net/10.18637/jss.v051.i03
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    References listed on IDEAS

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    1. Jing Qin, 2003. "Using logistic regression procedures for estimating receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 90(3), pages 585-596, September.
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    Cited by:

    1. Ben Kiprono Koech, 2021. "Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 1-18, March.
    2. Weilong Zhao & Xinwei Sher, 2018. "Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-28, November.

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