Annika Hoyer1, Antonia Zapf. 1. Department of Statistics, Ludwig-Maximilians-University Munich; Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf.
Abstract
BACKGROUND: The accurate diagnosis of a disease is a prerequisite for its appropriate treatment. How well a medical test is able to correctly identify or rule out a target disease can be assessed by diagnostic accuracy studies. METHODS: The main statistical parameters that are derived from diagnostic accuracy studies, and their proper interpretation, will be presented here in the light of publications retrieved by a selective literature search, supplemented by the authors' own experience. Aspects of study planning and the analysis of complex studies on diagnostic tests will also be discussed. RESULTS: In the usual case, the findings of a diagnostic accuracy study are presented in a 2 × 2 contingency table containing the number of true-positive, true-negative, false-positive, and true-positive test results. This information allows the calculation of various statistical parameters, of which the most important are the two pairs sensitivity/ specificity and positive/negative predictive value. All of these parameters are quotients, with the number of true positive (resp. true negative) test results in the numerator; the denominator is, in the first pair, the total number of ill (resp. healthy) patients, and in the second pair, the total number of patients with a positive (resp. negative) test. The predictive values are the parameters of greatest interest to phy - sicians and patients, but their main disadvantage is that they can easily be misinterpreted. We will also present the receiver operating characteristic (ROC) curve and the area under the curve (AUC) as additional important measures for the assessment of diagnostic tests. Further topics are discussed in the supplementary materials. CONCLUSION: The statistical parameters used to assess diagnostic tests are primarily based on 2 × 2 contingency tables. These parameters must be interpreted with care in order to draw correct conclusions for use in medical practice.
BACKGROUND: The accurate diagnosis of a disease is a prerequisite for its appropriate treatment. How well a medical test is able to correctly identify or rule out a target disease can be assessed by diagnostic accuracy studies. METHODS: The main statistical parameters that are derived from diagnostic accuracy studies, and their proper interpretation, will be presented here in the light of publications retrieved by a selective literature search, supplemented by the authors' own experience. Aspects of study planning and the analysis of complex studies on diagnostic tests will also be discussed. RESULTS: In the usual case, the findings of a diagnostic accuracy study are presented in a 2 × 2 contingency table containing the number of true-positive, true-negative, false-positive, and true-positive test results. This information allows the calculation of various statistical parameters, of which the most important are the two pairs sensitivity/ specificity and positive/negative predictive value. All of these parameters are quotients, with the number of true positive (resp. true negative) test results in the numerator; the denominator is, in the first pair, the total number of ill (resp. healthy) patients, and in the second pair, the total number of patients with a positive (resp. negative) test. The predictive values are the parameters of greatest interest to phy - sicians and patients, but their main disadvantage is that they can easily be misinterpreted. We will also present the receiver operating characteristic (ROC) curve and the area under the curve (AUC) as additional important measures for the assessment of diagnostic tests. Further topics are discussed in the supplementary materials. CONCLUSION: The statistical parameters used to assess diagnostic tests are primarily based on 2 × 2 contingency tables. These parameters must be interpreted with care in order to draw correct conclusions for use in medical practice.
Authors: Georg Fieseler; Kevin Laudner; Julia Sendler; Jakob Cornelius; Stephan Schulze; Wolfgang Lehmann; Souhail Hermassi; Karl-Stefan Delank; René Schwesig Journal: JSES Int Date: 2022-02-18