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.
Learning Objectives:
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.
- Discuss relevant 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
- Clinical/Laboratory Data Analysts
- Laboratory Data Managers
- 8:30 – 9:00 AM: Registration
- 9:00 AM: Session Start Time
- Lecture 1: Course Motivation and Overview of ICH Methodology (Including Q2A, Q2B, Q8 and Q9)
- 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
- Updated extreme variance detection strategies
- 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
- 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
Emeritus Professor of Biostatistics, University of Alabama
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.
Publications:
He is widely published with over 300 publications and some of his recent works include:
- Bartolucci, Al: Bayesian modeling of pharmaceutical data addressing the average effect of bivariate parameters of interest in a bioequivalence framework, page 166, December 2011, Journal of International Modeling and Simulation, Vol
- Bartolucci, Al: An application of EM algorithm in prostate carcinoma data, page 525, Epidemiology, Health and Medical Research
- Bartolucci, Al: Meta-analysis of multiple primary prevention trials of cardiovascular events using aspirin, page, American Journal of Cardiology
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