BioPharm International-05-01-2003

by Robert J. Seely, Amgen, Inc. Louis Munyakazi, John Haury, Heather Simmerman, W. Heath Rushing, and Thomas F. Curry Determining whether a data point is an "outlier" ? a result that doesn't fit, that is too high or too low, that is extreme or discordant ? is difficult when using small data sets (such as the data from three, four, or five conformance runs). The authors show that the Weisberg t-test is a powerful tool for detecting deviations in small data sets.

by James D. Williams, Virginia Polytechnic Institute and State University, Jeffrey B. Birch, and Steven Walfish Statistics don't lie, but if you don't include appropriate data, the resulting statistical analyses can be misleading. In biopharmaceutical development, if the variability from several different microplates is not addressed, assays testing the relative potency against a standard can be inaccurate. A statistical method that accounts for plate-to-plate heterogeneity is needed ? and presented here.

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by Wolfgang Winter, Agilent Technologies GmbH and Ludwig Huber, Agilent Technologies GmbH The rules for electronic records and signatures still remain in effect. The change means that companies must now justify their decisions on whether or not to implement specific electronic controls with documented risk assessments and considerations of the record requirements detailed in the corresponding predicate rule.