These results demonstrated that the DE statistics did not assess accurately the parallelism between the test sample and the
reference standard in that there were questionable acceptances.
Figure 3. Intact vs. Degraded RNA Divergence at most concentrations. Dilutional similarity rejected by F-test. Barely accepted
by DE test.
F-STATISTICS ARE BETTER
The DE measure assesses the parallelism at the linear part of the standard curve. It is a practical measurement that might
be a solution for the differences in matrix, such as different initial concentrations tested and different sets of concentrations
of references and test samples tested in the assay. However, fitting a logistic response and using only the linear part might
lead to a biased estimate during assay development. The use of DE measure in parallel-line assay with the appropriate test
design might be more appropriate.
Figure 4. Agilent 2100 Bioanalyzer Data of a High Quality, Eukaryotic, Total RNA Sample Dilutional similarity accepted.
F-statistics are very sensitive to the differences in matrix and should not be used when the reference and test sample are
not tested with the same concentrations. Set the acceptance criteria for parallelism according to the stage during test development.
The critical value is determined by the alpha level, which is the rejection rate assuming parallelism. Typical values for
alpha are 5%, 1% or 0.1%. In practice, assays are rejected too frequently for nonparallelism when alpha is 5%, but it is difficult
to see the nonparallelism. One could set the alpha level to 1% or 0.1% at the beginning of the test development and increase
alpha to 5% after all the parameters are under control.
Figure 5. Different Types of Genomic DNA Contamination In Total RNA Preparations Both tests reject dilutional similarity.
Departure from dilutional similarity can be interpreted as evidence that the groups of organisms are not comparable or the
preparations do not contain the same active compound. Failure of the parallelism test is often the first evidence that a product
is not stable. F- statistics can be used for full curves analysis to test for parallelism (but we recommend you test that
the asymptotes have not changed).
Use DE with caution. As DE uses only the slopes of the test sample and reference standard, conduct a data transformation and
a parallel line analysis.
Eloi P.Kpamegan,Ph.D.,MSF, Manager Biostatistics Unit, Global Clinical Immunology, North American Analytical Science and Assay Development Sanofi Pasteur,
One Discovery Drive, Swiftwater, PA 18370 570.839.4659,Fax:570.895.2972, email@example.com
1. Reeve R. Two statistical methods for estimating relative potency of bioassays. Biopharm Int. 2000 July; 13(7):54-60.
2. Emmens CW. The dose response relationship for certain principles of the pituitary gland, and of the serum and urine of
pregnancy. Journal of Endocr. 1940; 2:194-225.
3. Finney DJ. Response curves for radioimmunoassay. Clinical Chemistry 1983; 29:1562-1566.
4. Finney DJ. Statistical methods in biological assay, 3rd ed. London: Griffin; 1978.
5. Klein J, Capen R, Mancinelli R, Robinett R, Pietrobon FPJ, Quinn J, Schofield T. Validation of assays for use with combination
vaccines, Biologicals 1999; 27:35-41.
6. Schofield T. Assay Validation. In: Encyclopedia of Biopharmaceutical Statistics. New York: Marcel Dekker 2000; p. 21–30.