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Laboratory, Medical and Device Performance and Validation following Regulatory and ICH Statistical Guidelines (NTZ)

Laboratory, Medical and Device Performance and Validation following Regulatory and ICH Statistical Guidelines (NTZ)
DESCRIPTION

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.

Learning Objectives:
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
Lecture 1:
Overview of ICH Methodology
Lecture 2:
Introduction to the simple regression model
Interpreting the slope and intercept in validation procedures
Residual analysis and error detection
Stability and Potency issues
Lecture 3:
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
Lecture 4:
Confidence and tolerance bounds on risk models
Parametric and non parametric (non normal data) procedures
Exact computational techniques

Day 2 Schedule
Lecture 1:
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
Lecture 2:
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
Lecture 3:
Statistical process laboratory control and capability
Normal and non normal data procedures
Evolutionary Operations Process
Lecture 4:
Confidence and tolerance bounds on limits of risk

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.

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