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Using Relative Statistics and Approximate Disease Prevalence to Compare Screening Tests

Author

Listed:
  • Samuelson Frank

    (Division of Imaging, Diagnostics and Software Reliability, US Food and Drug Administration, 10903 New Hampshire Ave Building 62, Room 3102, Silver Spring, MD 20903-1058, USA)

  • Abbey Craig

    (Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA)

Abstract

Schatzkin et al. and other authors demonstrated that the ratios of some conditional statistics such as the true positive fraction are equal to the ratios of unconditional statistics, such as disease detection rates, and therefore we can calculate these ratios between two screening tests on the same population even if negative test patients are not followed with a reference procedure and the true and false negative rates are unknown. We demonstrate that this same property applies to an expected utility metric. We also demonstrate that while simple estimates of relative specificities and relative areas under ROC curves (AUC) do depend on the unknown negative rates, we can write these ratios in terms of disease prevalence, and the dependence of these ratios on a posited prevalence is often weak particularly if that prevalence is small or the performance of the two screening tests is similar. Therefore we can estimate relative specificity or AUC with little loss of accuracy, if we use an approximate value of disease prevalence.

Suggested Citation

  • Samuelson Frank & Abbey Craig, 2016. "Using Relative Statistics and Approximate Disease Prevalence to Compare Screening Tests," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-9, November.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:9:n:14
    DOI: 10.1515/ijb-2016-0017
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