This course is designed to introduce to individuals the understanding and interpretation of the statistical concepts with reference to certain quantitative ICH Guidelines that apply across laboratory (drug development) and clinical development (drug/device) procedures such as analytical methods in validation and acceptance criteria in calibration procedures, risk management and process monitoring as well as dealing with uncertainties and other relevant issues. It is not a course in statistics but introduces the participant to a hands on approach to the statistical techniques one uses, how they are applied and reasonably interpreted and understood. One will address the various challenges facing pharmaceutical and biotechnology companies when it comes to quantifying results in a meaningful interpretable manner through tabulations and graphical presentations.
In this two day workshop seminar one will learn the different regulatory agencies expectations of the quantification and development of a sound statistical monitoring of process control that are accepted, effective, and efficient. Participants will become familiar with the important aspects of the statistical methods and learn how organizations are expected to apply these guidelines.
Upon completing this course participants should be able to:
Evaluate linear and other quantitative measurement procedures.
Distinguish the difference between confidence and tolerance intervals.
Evaluate the sensitivity of the sample size in given procedures.
Evaluate laboratory/clinical data results based on risk management and design space issues.
Interpret alternative approaches to statistical process control in reference to data distributional formats.
Who will Benefit:
This course is designed for people responsible for developing, maintaining and/or improving clinical and laboratory monitoring programs and interpreting the results from such. This includes individuals that have data monitoring responsibilities. The following personnel will benefit from the course:
Assay Development Scientists
Clinical/Laboratory Data Analysts
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