title: Improving diagnostic accuracy: simple statistical nomograms to interpret medical literature.
abstract: BACKGROUND
  Authors of medical diagnostic literature frequently report sensitivity and specificity as measures of the quality of an evaluative study. However, these representations are easily misinterpreted by clinicians to be indicative of the prospective value of a test as predictive of the presence (positive predictive value, PPV) or absence of disease (negative predictive value, NPV). Although these phenomena are related, the mathematical expression and, therefore, the conclusions are more complex.
  
  
  METHODS
  Using algebraic methods, we derived simplified formulas to determine PPV, NPV, and accuracy (A). These general terms were solved by constraining individual variables, resulting in the development of curves that may be used routinely to analyze medical diagnostic literature.
  
  
  RESULTS
  Equations for PPV, NPV, and A were generated by using sensitivity, specificity, and incidence/prevalence as the dependent variables. These equations have been employed to generate representative graphs of PPV, NPV, and A and to clarify trends in these features with respect to commonly reported data.
  
  
  CONCLUSIONS
  These simplified equations allow clinicians to determine the utility of diagnostic studies in prospect, despite having only sensitivity, specificity, and incidence or prevalence of disease.
authors.affiliation: Department of Surgery, OHSU, Portland, OR, USA. blissd@ohsu.edu
authors.forename: David
authors.initials: D
authors.lastname: Bliss
authors.forename: Brian
authors.initials: B
authors.lastname: Diggs
authors.forename: Marla
authors.initials: M
authors.lastname: Matar
mesh_terms: D006801:Humans; D049451:Nomograms; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity