Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Evaluation of diagnostic tests for infectious diseases: general principles

Nature Reviews Microbiology volume 4, pages S21–S31 (2006)Cite this article

Abstract

The TDR Diagnostics Evaluation Expert Panel

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Essential elements in designing diagnostic test evaluations.

References

  1. Greenhalgh, T. How to read a paper: papers that report diagnostic or screening tests. Br. Med. J. 315, 540– 543 (1997).

    Article  CAS  Google Scholar 

  2. Borriello, S. P. Near-patient microbiological tests. Br. Med. J. 319, 298– 301 (1999).

    Article  CAS  Google Scholar 

  3. Bossuyt, P. M. et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD Initiative. Clin. Chem. 49, 1– 6 (2003).

    Article  CAS  Google Scholar 

  4. Bossuyt, P. M. et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin. Chem. 49, 7– 18 (2003).

    Article  CAS  Google Scholar 

  5. Smith, P. G. & Morrow, R. H., eds Field Trials of Health Interventions in Developing Countries: A Toolbox, (Macmillan, London, 1996).

    Google Scholar 

  6. Alonzo, T. D & Pepe, M. S. Using a combination of reference tests to assess the accuracy of a new diagnostic test. Statist. Med. 18, 2987– 3003 (1999).

    Article  CAS  Google Scholar 

  7. Pepe, M. S. Statistical Evaluation of Medical Tests for Classification and Prediction. (Oxford Univ. Press, 2003).

    Google Scholar 

  8. Gardner, M. J. & Altman, D. G. Estimating with confidence. Br. Med. J. 296, 1210– 1211 (1988).

    Article  CAS  Google Scholar 

  9. McGinn, T. et al. Tips for learners of evidence-based medicine: 3. Measures of observer variability (kappa statistic). Can. Med. Assoc. J. 171, 1369– 1373 (2004).

    Article  Google Scholar 

  10. WHO. Standard Operating Procedures for Clinical Investigators. UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (WHO/TDR). TDR/ TDP/SOP99.1 <[online]> (1999)

  11. WHO/Council for International Organizations of Medical Sciences. International Ethical Guidelines for Biomedical Research Involving Human Subjects (2002).

  12. Nuffield Council on Bioethics. The Ethics of Research Related to Healthcare in Developing Countries <[online]> (2002)

  13. WHO. Guidelines for Good Laboratory Practice. UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases TDR/ PRD/GLP/01.2 (WHO/TDR, Geneva, 2001).

  14. Delaney, B. C. et al. Systematic review of near-patient test evaluations in primary care. Br. Med. J. 319, 824– 827 (1999).

    Article  CAS  Google Scholar 

  15. Reid, M. C., Lachs, M. S. & Feinstein, A. Use of methodological standards in diagnostic test research. Getting better but still not good. J. Amer. Med. Assoc. 274, 645– 651 (1995).

    Article  CAS  Google Scholar 

  16. Small, P. M. & Perkins, M. D. More rigour needed in trials of new diagnostic agents for tuberculosis. Lancet 356, 1048– 1049 (2000).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We wish to thank Izabela Suder-Dayao for excellent secretarial support, and Robert Ridley and Giorgio Roscigno for support and guidance.

Author information

Authors and Affiliations

  1. Medicines Control Council of South Africa, Pretoria, South Africa

    Shabir Banoo

  2. Malaria and other Vector-borne and Parasitic Diseases, World Health Organization–Regional Office for the Western Pacific, Manila, Philippines

    David Bell

  3. Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands

    Patrick Bossuyt

  4. Veterinary School, University of Bristol, Bristol, UK

    Alan Herring

  5. Clinical Research Unit, London School of Hygiene and Tropical Medicine, London, UK

    David Mabey

  6. Division of Microbiology Devices, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA

    Freddie Poole

  7. Infectious Diseases Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK

    Peter G Smith

  8. Tulip Group of Companies, Goa, India

    N. Sriram

  9. US Naval Medical Research Unit 2, Jakarta, Indonesia

    Chansuda Wongsrichanalai

  10. Foundation for Innovative Diagnostics (FIND), Geneva, Switzerland

    Ralf Linke, Rick O'Brien & Mark Perkins

  11. UNICEF/UNDP/World Bank/WHO Special Programme for Research & Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland

    Jane Cunningham, Precious Matsoso, Carl Michael Nathanson, Piero Olliaro, Rosanna W. Peeling & Andy Ramsay

Authors
  1. Shabir Banoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Bell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Bossuyt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan Herring
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Mabey
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Freddie Poole
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peter G Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. N. Sriram
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chansuda Wongsrichanalai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ralf Linke
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rick O'Brien
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mark Perkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jane Cunningham
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Precious Matsoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Carl Michael Nathanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Piero Olliaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Rosanna W. Peeling
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Andy Ramsay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosanna W. Peeling.

Supplementary information

Glossary

Accuracy

The percentage of correct results obtained by the test under evaluation compared with the results of a reference or 'gold standard' test. Usually expressed as the number of correct results divided by the total number of results, multiplied by 100.

Blinding

Interpreting a test result without knowledge of a patient's condition or previous test results.

Confidence interval

The confidence interval quantifies the uncertainty in measurement; usually reported as the 95% confidence interval, the range that we can be 95% certain covers the true value.

Negative predictive value (NPV)

The probability that a negative result accurately indicates the absence of infection.

Positive predictive value (PPV)

The probability that a positive result accurately indicates the presence of infection.

Prevalence

The proportion of a given population with an infection at a given time.

Proficiency panel

A collection of six or more mock or true specimens with positive and negative results for a particular test, used to ascertain the proficiency of the technologist in performing the test.

Quality assurance (QA)

An ongoing process of monitoring a system for reproducibility or reliability of results, with which corrective action can be instituted if standards are not met.

Reference standard

The best available approximation of a true result, generally indicating a test method that is currently accepted as reasonably, but not necessarily, 100% accurate. It is used as the reference method for assessing the performance characteristics of another test method.

Reproducibility

A measure of the extent to which replicate analyses using identical procedures agree with each other.

Sensitivity

The probability (percentage) that patients with the infection (determined by the result of the reference or 'gold standard' test) will have a positive result using the test under evaluation.

Specificity

The probability (percentage) that patients without the infection (determined by the result of the reference or 'gold standard' test) will have a negative result using the test under evaluation.

Tests

Any method for obtaining additional information regarding a patient's health status.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Banoo, S., Bell, D., Bossuyt, P. et al. Evaluation of diagnostic tests for infectious diseases: general principles. Nat Rev Microbiol 4 (Suppl 9), S21–S31 (2006). https://doi.org/10.1038/nrmicro1523

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrmicro1523

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing