Recently we’ve been busy updating Analyse-it to stay aligned with the latest updates to the CLSI protocols, and added a new inverse prediction feature.
If you have active maintenance you can download and install the update now, see updating the software or visit the download page . If maintenance on your license has expired you can renew it to get this update and forthcoming updates, see renew maintenance.
New CLSI EP6-Ed2
The CLSI recently released guideline EP06-Ed2 on the Evaluation of Linearity of Quantitative Measurement Procedures , which replaces the EP06-A published in 2003.
EP06-A relied on fitting a linear (straight line), 2nd (parabolic) and 3rd (sigmoidal) order polynomials to the data. A method was then determined to be linear or possibly non-linear based on statistical criteria. The degree of nonlinearity was then calculated as the difference between the linear fit and the best fitting non-linear model (parabolic or sigmoidal curves). Nonlinearity could then be compared against allowable nonlinearity criteria.
The new CLSI EP6-Ed2 protocol no longer requires fitting polynomial models to determine linearity. Instead, the deviation from linearity is calculated as the difference between the mean of each level and a linear fit through the data. That can then be compared against the allowable nonlinearity criteria. Other changes to the protocol include experimental design and there is now more focus on the structure of the variance across the measuring interval.
By default, the settings in Analyse-it are still configured for EP6-A as that is still in widespread use. However, to perform a new EP6-Ed2 analysis follow these steps:
Note: Computation of linearity only requires the relationship between the levels, so you do not need to enter the assigned values if they are unknown.
New Inverse prediction feature
This release also includes a new inverse prediction feature for simple linear regression models. Inverse prediction has several uses including estimating the shelf-life of a product. In the context of CLSI protocols it is useful for CLSI EP25-A – Evaluation of Stability of In Vitro Diagnostic Reagents .
To make an inverse prediction use Fit Model to fit a simple regression model, then:
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