We are receiving a lot of questions about relevant analyses in the Analyse-it Method Validation edition to help in evaluating new diagnostic tests in the fight against COVID-19. Below are some quick links that will help, but contact us if you have questions - we are working as normal.

CLSI EP12-A2 for diagnostic accuracy see Sensitivity/Specificity, Estimating the sensitivity and specificity of a binary test and Comparing the accuracy of two binary diagnostic test.
CLSI EP17-A2 for detection limits of molecular methods using Probit analysis, see: Estimating the detection limit using a Probit fit.

EP12-A2 example sensitivity specificity of 2 diagnostic tests

EP17-A2 example Probit detection limit

Robust bi-weight quantile

A quantile estimator that uses robust estimators of location and spread, which resist the effects of extreme observations.

The robust bi-weight estimator (Horn & Pesce 1998) uses robust estimators of location and spread, which resist the effects of extreme observations. It requires a symmetric distribution. If the distribution is skewed, you should apply a transform to achieve symmetry, or create a new sample by mirroring the sample around the median to form a symmetric distribution (Horn, 1990), before applying the bi-weight estimator.

Related information

Horn, P. S., & Pesce, A. J. (2005). Reference intervals: a user's guide. American Association for Clinical Chemistry.

Horn, P. S. (1990). Robust quantile estimators for skewed populations. Biometrika, 77(3), 631-636.

Horn, P. S., Pesce, A. J., & Copeland, B. E. (1998). A robust approach to reference interval estimation and evaluation. Clinical Chemistry, 44(3), 622-631.

Horn, P. S., Pesce, A. J., & Copeland, B. E. (1999). Reference interval computation using robust vs. parametric and nonparametric analyses. Clinical Chemistry, 45(12), 2284-2285.

Available in Analyse-it Editions
Method Validation edition
Ultimate edition