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, Predictive values, Estimating the sensitivity and specificity of a binary test and Comparing the accuracy of two binary diagnostic test.
CLSI EP17-A2 for detection capability of molecular methods using Probit analysis, see: Estimating the detection limit using a Probit fit and may be useful for determining the cutoff of a method with an internal continuous response see: Estimating the detection limit of a measurement system and Estimating the detection limit of a measurement system using a precision profile.

Also see our latest blog post: Sensitivity/Specificity and The Importance of Predictive Values for a COVID-19 test

EP12-A2 Example: Sensitivity specificity of 2 diagnostic tests

EP17-A2 Example 1:
LoB and LoD

EP17-A2 Example 2:
LoD from precision profile

EP17-A2 Example 4:
Probit detection limit

Fitting ordinary linear regression (method comparison)

Fit a ordinary least square regression to estimate the relationship between a test method and a reference method when the reference method is measured without error.

Select a cell in the dataset.

On the Analyse-it ribbon tab, in the Statistical Analyses group, click Method Comparison, and then click the fit:

Option	Description
Ordinary Least Square	Fit an ordinary regression where the test method measurement error SD is constant throughout the measuring interval.
Weighted Least Squares	Fit an ordinary regression where the test method measurement error CV is constant throughout the measuring interval.

The analysis task pane opens.

If the data are in 2 variables:
1. In the X drop-down list, select the comparative or reference measurement procedure variable.
2. In the Y drop-down list, select the test measurement procedure variable.
Note: If the variables consist of replicate measurements, select the variable name that spans all the replicate columns.
If the data are in 2 variables with a separate variable identifying replicates of each item:
1. In the X drop-down list, select the comparative or reference measurement procedure variable.
2. In the Y drop-down list, select the test measurement procedure variable.
3. In the Item drop-down list, select the variable identifying each item.
If the data are in a single variable with a separate variable matching each item and a variable identifying the method:
1. In the Model drop-down menu, select Matched Pairs.
2. In the Y drop-down list, select the measurement variable.
3. In the Item drop-down list, select the item variable that identifies each item.
4. In the Method drop-down list, select the method variable.

If the items are measured in replicate, in the Replicates group, select:

Option	Description
1st X, 1st Y	Uses only the 1st X replicate and 1st Y replicate in the regression.
Mean X, 1st Y	Uses the Mean of X replicates and the 1st Y replicate in the regression.
Mean X, Mean Y	Uses the Mean of the X replicates and the Mean of the Y replicates in the regression.

Note: All items must have the same number of replicates. Items that do not are excluded from analysis.

Click Calculate.

Related concepts

Ordinary linear regression

Related tasks

Checking the assumptions of the fit

Estimating the bias between methods at a decision level

Partitioning and reducing the measuring interval

Related reference

Dataset layout

Related information

CLSI. (2002). Method Comparison and Bias Estimation Using Patient Samples (EP9-A2). Clinical and Laboratory Standards Institute.

Available in Analyse-it Editions
Method Validation edition
Ultimate edition