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

Estimators for correlation and association

Estimators for correlation/association and their properties and assumptions.

Estimator	Purpose
Pearson r	Estimate the Pearson rho correlation coefficient using the sample Pearson correlation coefficient r. Use when a linear function best describes the relationship between the variables. Susceptible to outliers. Assumes a bivariate normal distribution.
Spearman rs	Estimate the Spearman rho rank correlation coefficient using the sample Spearman rs correlation coefficient. Use when a monotonic function best describes the relationship between variables.
Kendall tau	Estimate the Kendall tau rank correlation coefficient. Use when a monotonic function best describes the relationship between the variables. Preferred instead of Spearman correlation because Kendall's tau is the difference between the probability that the two variables are in the same order, versus the probability that the two variables are in different orders. Robust to outliers.

Estimator

Purpose

Pearson r

Estimate the Pearson rho correlation coefficient using the sample Pearson correlation coefficient r.

Use when a linear function best describes the relationship between the variables.

Susceptible to outliers. Assumes a bivariate normal distribution.

Spearman rs

Estimate the Spearman rho rank correlation coefficient using the sample Spearman rs correlation coefficient.

Use when a monotonic function best describes the relationship between variables.

Kendall tau

Estimate the Kendall tau rank correlation coefficient.

Use when a monotonic function best describes the relationship between the variables. Preferred instead of Spearman correlation because Kendall's tau is the difference between the probability that the two variables are in the same order, versus the probability that the two variables are in different orders.

Robust to outliers.

Related concepts

Parameter estimate

Related tasks

Estimating the correlation/association

Available in Analyse-it Editions
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition