Article Text

Download PDFPDF
Making sense of diagnostic tests likelihood ratios
  1. Rafael Perera, DPhil,
  2. Carl Heneghan, BA, MRCGP
  1. University of Oxford Department of Primary Health Care
 Oxford, UK

    Statistics from

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

 A smart mother makes often a better diagnosis than a poor doctor.
 August Bier German, professor of surgery 1861–1949.

    Statistical diagnosis is a mystery to many health practitioners.1 Information is often presented as test accuracy (sensitivity and specificity). However, at the bedside we need to know how a test result predicts the diagnosis or outcome of interest. Attempts to demystify approaches to diagnosis include the construction of 2 by 2 boxes, nomograms, and rules such as SpPin (with a Specific test the Positive Rules in) and SnNout (with a Sensitive test the Negative Rules out). In our teachings of evidence-based medicine, we have found an easier, intuitive way to interpret the results of diagnostic studies based on 2 elements: the likelihood ratio of the test and the pretest odds.

    The likelihood ratio (LR) summarises information about the diagnostic test by combining information about the sensitivity and specificity. It tells you how much a positive or negative result changes …

    View Full Text