Evaluation of nucleic acid amplification tests in the absence of a perfect gold-standard test: a review of the statistical and epidemiologic issues

Epidemiology. 2005 Sep;16(5):604-12. doi: 10.1097/01.ede.0000173042.07579.17.


During the past 10 years, medical diagnostic testing for sexually transmitted infections (STIs) has changed markedly as a result of the rapid expansion and marketing of nucleic acid amplification tests (NAATs). Among such new DNA/RNA-amplification techniques are the polymerase chain reaction (PCR), the ligase chain reaction (LCR), and the transcription-mediated amplification (TMA) tests. Regrettably, the test evaluation process undergone by these tests has not always been rigorous or scientifically sound. Here, we review the controversy surrounding the statistical evaluation of these NAATs. We also review some of the traditional and recent statistical methods developed to estimate test sensitivity and specificity parameters in the absence of reliable gold-standard tests. In particular, we review the traditional latent class modeling approach that requires the assumption of independence between diagnostic tests conditional on the true disease status, and the more recent procedures that relax the conditional independence assumption. Finally, we apply some of these statistical modeling techniques to real data to estimate the sensitivity and specificity of a NAAT for Chlamydia trachomatis. On the basis of the latent class modeling approach with a pessimistic prior for culture sensitivity, the NAAT specificity estimate was 97.6% and, on the basis of an optimistic prior, the specificity was 95.3%. Similarly, the sensitivity estimates ranged from 88.1% to 89.6%.

Publication types

  • Evaluation Study

MeSH terms

  • Biometry
  • Chlamydia Infections / diagnosis*
  • Chlamydia Infections / microbiology
  • Chlamydia trachomatis / isolation & purification*
  • Female
  • Humans
  • Models, Statistical*
  • Nucleic Acid Amplification Techniques / methods
  • Nucleic Acid Amplification Techniques / standards*
  • Reproducibility of Results
  • Sensitivity and Specificity