An investigation of new toxicity test method performance in validation studies: 3. Sensitivity and specificity are not independent of prevalence or distribution of toxicity

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification.

Diagnosis: Toxic! – Trying to Apply Approaches of Clinical Diagnostics and Prevalence in Toxicology Considerations

The assessment of relevance of toxicological testing was compared with approaches of diagnostic medicine, a discipline that faces a comparable situation. Considering the work of a toxicologist as setting a diagnosis for compounds, assessment tools for diagnostic tests were transferred to toxicological tests. In clinical diagnostics, test uncertainty is well accepted and incorporated in this assessment. Furthermore, prevalence information is considered to evaluate the gain in information resulting from the application of a test. Several common toxicological scenarios, in which test uncertainty and prevalence are combined, are discussed including the interdependence of test accuracy, prevalence and predictive values or the sequential application of a screening and a confirmatory test. In addition, real prevalences derived from prevalences determined by an imperfect test are presented. We conclude that information on prevalences of toxic health effects is required to allow a complete assessment of the relevance of toxicological test. In this process, lessons can be learned from evidence-based approaches in clinical diagnostics.