Skip to main content

Advertisement

Log in

Receiver operating characteristic (ROC) curve for medical researchers

  • Perspective
  • Published:
Indian Pediatrics Aims and scope Submit manuscript

Abstract

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Reddy S, Dutta S, Narang A. Evaluation of lactate dehydrogenase, creatine kinase and hepatic enzymes for the retrospective diagnosis of perinatal asphyxia among sick neonates. Indian Pediatr. 2008;45:144–147.

    PubMed  Google Scholar 

  2. Sood SL, Saiprasad GS, Wilson CG. Mid-arm circumference at birth: A screening method for detection of low birth weight. Indian Pediatr. 2002;39:838–842.

    PubMed  CAS  Google Scholar 

  3. Randev S, Grover N. Predicting neonatal hyperbilirubinemia using first day serum bilirubin levels. Indian J Pediatr. 2010;77:147–150.

    Article  PubMed  Google Scholar 

  4. Vasudevan A, Malhotra A, Lodha R, Kabra SK. Profile of neonates admitted in pediatric ICU and validation of score for neonatal acute physiology (SNAP). Indian Pediatr. 2006;43:344–348.

    PubMed  Google Scholar 

  5. Khanna R, Taneja V, Singh SK, Kumar N, Sreenivas V, Puliyel JM. The clinical risk index of babies (CRIB) score in India. Indian J Pediatr. 2002;69:957–960.

    Article  PubMed  Google Scholar 

  6. Indrayan A. Medical Biostatistics (Second Edition). Boca Raton: Chapman & Hall/CRC Press; 2008. p. 263–267.

    Google Scholar 

  7. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.

    PubMed  CAS  Google Scholar 

  8. Campbell G. Advances in statistical methodology for evaluation of diagnostic and laboratory tests. Stat Med. 1994;13:499–508.

    Article  PubMed  CAS  Google Scholar 

  9. Bamber D. The area above the ordinal dominance graph and area below the receiver operating characteristic graph. J Math Psychol. 1975;12:387–415.

    Article  Google Scholar 

  10. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the area under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845.

    Article  PubMed  CAS  Google Scholar 

  11. Cleves MA. Comparative assessment of three common algorithms for estimating the variance of the area under the nonparametric receiver operating characteristic curve. Stata J. 2002;3:280–289.

    Google Scholar 

  12. Box GEP, Cox DR. An analysis of transformation. J Royal Statistical Society, Series B. 1964;26:211–252.

    Google Scholar 

  13. Zhou Xh, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine. New York: John Wiley and Sons, Inc; 2002.

    Book  Google Scholar 

  14. Metz CE, Herman BA, Shen JH. Maximum likelihood estimation of receiver operating characteristic (ROC) curve from continuously distributed data. Stat Med. 1998;17:1033–1053.

    Article  PubMed  CAS  Google Scholar 

  15. ROCKIT [Computer program]. Chicago: University of Chicago. Available from: www-radiology.uchicago. edu/krl/KRL_ROC/software_index6.htm. Accessed on February 27, 2007.

    Google Scholar 

  16. Faraggi D, Reiser B. Estimating of area under the ROC curve. Stat Med. 2002;21:3093–3106.

    Article  PubMed  Google Scholar 

  17. Hajian Tilaki KO, Hanley JA, Joseph L, Collet JP. A comparison of parametric and nonparametric approaches to ROC analysis of quantitative diagnosis tests. Med Decis Making. 1997;17:94–102.

    Article  PubMed  CAS  Google Scholar 

  18. Pepe M, Longton G, Janes H. Estimation and comparison of receiver operating characteristic curves. Stata J. 2009;9:1.

    PubMed  Google Scholar 

  19. Jiang Y, Metz CE, Nishikawa RM. A receiver operating characteristic partial area index for highly sensitive diagnostic test. Radiology. 1996;201:745–750.

    PubMed  CAS  Google Scholar 

  20. Youden WJ. An index for rating diagnostic test. Cancer. 1950;3:32–35.

    Article  PubMed  CAS  Google Scholar 

  21. Perkins NJ, Schisterman EF. The inconsistency of ‘optimal’ cut points obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163:670–675.

    Article  PubMed  Google Scholar 

  22. Whiting P, Ruljes AW, Reitsma JB, Glas AS, Bossuyt PM, Kleijnen J. Sources of variation and bias in studies of diagnostic accuracy - a systematic review. Ann Intern Med. 2004;140:189–202.

    PubMed  Google Scholar 

  23. Kelly S, Berry E, Proderick P, Harris KM, Cullingworth J, Gathercale L, et al. The identification of bias in studies of the diagnostic performance of imaging modalities. Br J Radiol. 1997;70:1028–1035.

    PubMed  CAS  Google Scholar 

  24. Malaria Site. Peripheral smear study for malaria parasites - Available from: www.malariasite.com/malaria/DiagnosisOfMalaria.htm. Accessed on July 05, 2010.

  25. Thomas S, Srivastava A, Jeyaseelan L, Dennison D, Chandy M. NESTROFT as screening test for the detection of thalassaemia & common haemoglobinopathies - an evaluation against a high performance liquid chromatographic method. Indian J Med Res. 1996;104:194–197.

    PubMed  CAS  Google Scholar 

  26. Bachmann LM, Puhan MA, ter Riet G, Bossuyt PM. Sample sizes of studies on diagnostic accuracy: literature survey. BMJ. 2006;332:1127–1129.

    Article  PubMed  Google Scholar 

  27. Malhotra RK, Indrayan A. A simple nomogram for sample size for estimating the sensitivity and specificity of medical tests. Indion J Ophthalmol. 2010;58:519–522.

    Article  Google Scholar 

  28. Kester AD, Buntinx F. Meta analysis of curves. Med Decis Making. 2000;20:430–439.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajeev Kumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, R., Indrayan, A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr 48, 277–287 (2011). https://doi.org/10.1007/s13312-011-0055-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13312-011-0055-4

Key words

Navigation