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Laboratory, Medical and Device Performance and Validation following Regulat...
Thu, Jul 27, 2017, 8:30 AM – Fri, Jul 28, 2017, 4:30 PM EDT
Course "Laboratory, Medical and Device Performance and Validation following Regulatory and ICH Statistical Guidelines" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.
This course is designed to introduce to individuals the understanding and interpretation of the statistical concepts one uses when investigating quantitative ICH Guidelines such as analytical methods validation, procedures and acceptance criteria in calibration limits, and process and quality control. One also considers ICH Q8 and Q9. These techniques covers both clinical and laboratory applications. This applies to many areas such as stability testing, outlier analysis and risk management. It is not a course in statistics but introduces the participant to an applied approach to the statistical techniques one uses, how they are reasonably interpreted. 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 utilized, effective, and efficient. Participants will become familiar with the important aspects of the statistical methods and learn how these guidelines are applied in practice.
- Evaluate linear quantitative measurement procedures and sources of error.
- Distinguish the difference between confidence and tolerance intervals
- Evaluate the appropriateness of the effect of sample size in given procedures.
- Evaluate laboratory/clinical quality control based on risk management
- Interpret statistical process control
- Distinguish between FDA requirements and ICH guidelines
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:
- Quality Managers
- Quality Professionals
- Assay Development Scientists
- Research Scientists
- Data Analysts
- Laboratory Data Managers
Day 1 Schedule
Overview of ICH Methodology
Introduction to the simple regression model
- Interpreting the slope and intercept in validation procedures
- Residual analysis and error detection
- Stability and Potency issues
Outlier strategies using the linear model in calibration methods
- Imputation techniques for missing data
- Outlier strategies for non normal or ranked data
- Consequences of outlier elimination/substitution
- Sample size and analysis issues
Confidence and tolerance bounds on risk models
- Parametric and non parametric (non normal data) procedures
- Exact computational techniques
Day 2 Schedule
Discussion of risk management in general
- Traditional risk management strategies in clinical settings
- Predictive models in risk assessment
- Discussion of the Design Space
- Risk Management in pre-analytical phase of laboratory testing
Introduction to validation of models in hazard assessment and risk management
- Demonstration of laboratory Validation procedures
- Bivariate models and confusion matrices and derived statistics
- ROC plot
Statistical process laboratory control and capability
- Normal and non normal data procedures
- Evolutionary Operations Process
Confidence and tolerance bounds on limits of risk
Al Bartolucci, Ph.D.
Dr. Al Bartolucci is Emeritus Professor of Biostatistics at the University of Alabama where he also serves as a Senior Scientist at the Center for Metabolic Bone Diseases, AIDS Research Center and Cancer Center.
He previously served as Chairman of the Department from 1984 through 1997. He has also taught Statistical Software courses involving Data Exploration, ANOVA/Regression and Design of Experiments. His teaching experience includes areas such as, Clinical Trials, Survival Analysis, Multivariate Analysis, Regression Techniques and Environmental/Industrial Hygiene Sampling and Analysis, Bayesian Statistics, and Longitudinal Data Analysis.
Dr. Bartolucci received his PhD in Statistics from the State University of New York at Buffalo and his MA in Mathematics from Catholic University, Washington DC, and his BA in Mathematics from Holy Cross.
NO REFUNDS ON REGISTRATIONS ALLOWED
NO TRANSFER ON REGISTRATIONS ALLOWED
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