Evidence-based emergency medicine/skills for evidence-based emergency care. Interval likelihood ratios: another advantage for the evidence-based diagnostician.
Emergency physicians are often confronted with making diagnostic decisions on the basis of a test result represented on a continuous scale. When the results of continuous data are expressed as binary outcomes using a single cutoff, loss of information and distortion may occur. In this setting, interval likelihood ratios provide a distinct advantage in interpretation over those based on a dichotomized sensitivity and specificity. Dividing the data into intervals uses more of the information contained in the data and allows the clinician to more appropriately interpret the test results and to make valid clinical decisions. This article illustrates the advantages of interval likelihood ratios with examples and demonstrates how to calculate them on the basis of different data formats. Authors and journals need to be encouraged to report the results of studies of performance of diagnostic tests using interval ranges rather than simple dichotomization when the tests involve continuous variables.
Brown
Michael D
MD
Grand Rapids MERC/Michigan State University Program in Emergency Medicine, Grand Rapids, MI, USA. brownm@msu.edu
Reeves
Mathew J
MJ
D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D003955:Diagnostic Tests, Routine / Q000592:standards*; D004635:Emergency Medicine / Q000379:methods; D019317:Evidence-Based Medicine / Q000379:methods; D006801:Humans; D016013:Likelihood Functions; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity