This website consolidates several links on test validation. Test validation confirms that the analytical procedure employed for a specific test is suitable for its intended use. FDA regulations such as GMP, GLP and GCP and quality standards such as ISO17025 require analytical methods to be validated before and during routine use. There are no specific regulations on method validations but the FDA, other agencies and industry task forces have developed guidelines for method validation. This site provides links to such guidelines.

This IUPAC technical report is the result of the Symposium on Harmonization of Quality Assurance Systems for Analytical Laboratories, held in Budapest, Hungary, 4-5 November 1999, under the sponsorship of IUPAC, ISO, and AOAC International.

In this review, the authors critically discuss the objectives, methods and limitations of different approaches for the validation of diagnostic tests. Also, the writers show (based on published data and our own experiences) that estimates for the diagnostic sensitivity and specificity may vary among populations and/or subpopulations of animals, conditional on the distribution of influential covariates.

This study calculates conditional covariances as a measure of dependence between binary tests and shows their relationship. The writers use published data for toxoplasmosis and brucellosis in swine, and Johne’s disease in cattle to illustrate calculation methods and to indicate the likely magnitude of the dependence between serologic tests used for diagnosis and surveillance of animal diseases.

The investigators describe the development of a program (implemented in R and S-Plus software) for this purpose that yields maximum-likelihood estimates of sensitivity, specificity and prevalence. Also implemented includes an HTML form, which submits data to a web-based interface to R.

In this study, the authors propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.

In this paper, the writers develop statistical methods for evaluating diagnostic test accuracy and prevalence estimation based on finite sample data in the absence of a gold standard. Moreover, two tests are often applied simultaneously for the purpose of obtaining a joint testing strategy that has either higher overall sensitivity or specificity than either of the two tests considered singly. Sequential versions of such strategies are often applied in order to reduce the cost of testing. Thus, joint (simultaneous and sequential) testing strategies and inference for them are discussed.

Here, recent Bayesian approaches to estimation (based on cross-sectional sampling designs) of the sensitivity and specificity of one or more diagnostic tests are reviewed. The primary goal is to provide veterinary researchers with a concise presentation of the computational aspects involved in using the Bayesian framework for test evaluation. The authors consider estimation of diagnostic-test sensitivity and specificity in the following settings: (i) one test in one population, (ii) two conditionally independent tests in two or more populations, (iii) two correlated tests in two or more populations, and (iv) three tests in two or more populations, where two tests are correlated but jointly independent of the third test.

Here, the authors developed an Excel spreadsheet template to calculate sample sizes to estimate sensitivity and specificity with desired precision in the absence of a gold standard. Calculations are predicated on the use of two conditionally independent tests for screening animals from two populations and are based on the methods produced by a previous study.

This article presents a Bayesian discrete mixed model for the estimation of covariate-pattern-specific variance partition coefficient (VPCs) when measurement of the infection/disease is based on an imperfect test. The utility of the presented model is demonstrated with three applications.

In the present study, the authors provided analytical formulae that give insight into the effect of assuming constant sensitivity when this assumption was false. To further investigate the effect of failure of the assumption of constant sensitivity, the investigators also simulated several data sets under the assumption that the first test's sensitivity varied in the two populations.

Proposed rule to change analysis and sampling procedures in wastewater regulations and to change methods in drinking water analysis and monitoring. There is also a request for comments on "EPA Microbiological Alternate Test Procedure (ATP) Protocol for Drinking Water, Ambient Water, and Wastewater Monitoring Methods"