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Manufacturers of foods, drugs, consumer goods, and other products must determine the shelf life of their products so that customers know when the product can be expected to perform as intended. Many approaches are available to quantify the "shelf life" and the method(s) chosen often depend on the testing time available.
This webinar discusses the steps to set-up a stability study and analyze the results to estimate the product's shelf life. The use of regression models to model the relationship between the response variable(s) and time are presented. Models useful for describing non-linear degradation over time are also presented. Additionally, methods for handling non-normal response data are also discussed. Finally, the use of accelerating variables to shorten the study time and the models required are introduced. The webinar includes several examples to illustrate the methods discussed
Companies must ensure that the advertised shelf life on their products is accurate and supported by data. Failure to do so may result in fraudulent claims, customer dissatisfaction, or even safety concerns. Many industries must comply with government or industry guidelines for determining shelf life.
This webinar provides an overview of statistical methods that are appropriate for shelf life determination. Both regression modeling of stability data and life data analysis are presented as valid methods for quantifying shelf life at a specified level of confidence.
Examples are included with illustrations to clarify what are typically confusing points. Also, the interpretation and communication of results will be stressed.