ROC curves in clinical chemistry: uses, misuses, and possible solutions.
BACKGROUND
ROC curves have become the standard for describing and comparing the accuracy of diagnostic tests. Not surprisingly, ROC curves are used often by clinical chemists. Our aims were to observe how the accuracy of clinical laboratory diagnostic tests is assessed, compared, and reported in the literature; to identify common problems with the use of ROC curves; and to offer some possible solutions.


METHODS
We reviewed every original work using ROC curves and published in Clinical Chemistry in 2001 or 2002. For each article we recorded phase of the research, prospective or retrospective design, sample size, presence/absence of confidence intervals (CIs), nature of the statistical analysis, and major analysis problems.


RESULTS
Of 58 articles, 31% were phase I (exploratory), 50% were phase II (challenge), and 19% were phase III (advanced) studies. The studies increased in sample size from phase I to III and showed a progression in the use of prospective designs. Most phase I studies were powered to assess diagnostic tests with ROC areas >/=0.70. Thirty-eight percent of studies failed to include CIs for diagnostic test accuracy or the CIs were constructed inappropriately. Thirty-three percent of studies provided insufficient analysis for comparing diagnostic tests. Other problems included dichotomization of the gold standard scale and inappropriate analysis of the equivalence of two diagnostic tests.


CONCLUSIONS
We identify available software and make some suggestions for sample size determination, testing for equivalence in diagnostic accuracy, and alternatives to a dichotomous classification of a continuous-scale gold standard. More methodologic research is needed in areas specific to clinical chemistry.
Obuchowski
Nancy A
NA
Department of Biostatistics, Division of Radiology-Wb4, The Cleveland Clinic Foundation, Cleveland, OH 44195-5196, USA. nobuchow@bio.ri.ccf.org
Lieber
Michael L
ML
Wians
Frank H
FH
D002624:Chemistry, Clinical / Q000379:methods*; D006801:Humans; D010506:Periodicals as Topic / Q000706:statistics & numerical data*; D012372:ROC Curve