Industry Leading 5 Parameter Logistic Model Improves Accuracy

StatLIA’s weighted 5 parameter logistic curve fitting model has been used for years by many of the most regulated, demanding biopharmaceutical laboratories in the world to compute their validated assays.  Brendan’s scientists and mathematicians have developed and improved the mathematical algorithms over the years to develop a robust, highly accurate 5PL model that consistently defines the best possible curve (global minimum) every assay – even with ill-behaved data. Read more...

StatLIA's 5 parameter and 4 parameter logistic models are so accurate and reliable that instrument manufactures have licensed them to incorporate into their software. StatLIA's 5 parameter logistic model can expand the dynamic range of an assay, especially at the low end, just by using it instead of other less-effective 5PL models. Read Bio-Rad Tech Note 3022.

Custom Weighting

Sample concentrations computed from unweighted curve fitting procedures can differ from properly weighted curves by hundreds of percent.  This is why StatLIA provides custom weighting by using a lab’s previous assays for each test to compute the best weighting models and calculate which best describes the variance. Read more...

Standard curve graph compares the assay’s standard curve to a historical set of assays (reference assays) for the same method.  The graph includes data points (boxes) from current assay, the standard curve established from the reference assays (dotted line), and 95% & 99% confidence limits based on the reference standard curve.   The graph enables labs to quickly view at a glance the reliability of today's assay.

Full Logistic Curve Parallelism

StatLIA’s new Chi-Square method for full logistic curve parallelism analysis provides, for the first time, a direct measure of the amount of nonparallelism present between two curves.  It also significantly improves the accuracy and reliability for determining parallelism and relative potency, overcoming the limitations of the F test and other methods.  Using the well characterized extra-sum-of-squares approach, the Chi-Square method is effective whether the data are from well-behaved immunoassay or an ill-behaved cell based assay. Read more...