Dr Christopher Burgess, Burgess Analytical Consultancy Limited, UK
Dr Bernd Renger, Bernd Renger Consulting, Germany
Dr Lance Smallshaw, UCB Biopharma sprl, Belgium
Dr Lori McCaig, Roche/Genentech, USA
The ECA Working Group on Analytical Quality Control was founded in 2010 in order to generate a harmonised SOP on managing analytical deviations within the laboratory including OOS, OOE and OOT results.
Version 2 of the ECA OOS SOP is already available for all ECA members since 2013.
Given the complexity of the topic, it was decided that the handling of OOT and OOE results should be addressed in a separate guideline SOP, since there is both a lack of knowledge in the industry and a lack of guidance for trend analysis from the regulators in spite of increased regulatory interest in this area.
In 2013 the ECA’s QC Working Group decided to address these issues by developing a new guideline aimed at QC and other quality groups to encourage the application of a consistent and scientifically sound approach to trend analysis as part of a QMS for assuring data integrity.
There were initially three core components:
Recommended approaches for detecting out of expectation (OOE) data within an analytical sequence which are based on the known process capability of the analytical procedure used.
Recommended approaches to detecting out of trend (OOT) data between analytical sequences where no trend is expected. These are based on standard Statistical Process Control methodology and Recommended approaches for detecting out of trend (OOT) data between analytical sequences where a trend is expected as is the case for Stability Testing.
From this foundation the current OOE-/ OOT-Laboratory Data Management Guidance was developed by an international team to provide a harmonised approach to trending.
At this ECA OOE/OOT Training Course version 01 of the OOE-/ OOT-Laboratory Data Management Guidance will be presented and participants will have the opportunity to review and discuss the contents and technical aspects of the guidance document as well as looking at the scope and application of the proposed methods within industry.
The ECA QC Working Group’s goal is to have a basic global framework for OOT/OOE within R&D, production and QC laboratories which is acceptable to the authorities and adaptable for individual companies.
This conference is intended for technical and managerial personnel dealing with out-of-trend or out-of expectation results, including R&D, production, analytical laboratories, contract laboratories, and Quality Assurance/Quality Control personnel.
Introduction to ECA’s Analytical QC Working Group and the OOT Process
Regulatory Importance of Trend Analysis under the EU GMPs
- Overview of ECA’s Analytical QC Working Group
- Data quality management in the Laboratory
- Structure of the OOT/OOE guideline generation process
- Importance of a Technical Glossary
- Overview of the contents of the OOT/OOE Guideline
- Aims and objectives for this Forum
The Statistical Tool Box; Basis for Selection
- Regulatory concern for the control of processes
- Overview of the cited regulatory references
- Challenges for implementation and inspection: within the industry - for the inspectorate
Recommendations on: Out of Expectation Results (OOE)
- What is a trend?
- What is a control chart?
- Data types
- Data distributions
- Statistical control: Common cause variation, Special cause variation
- Process stability versus process capability
Recommendations for Process Control of Variables
- Definitions for OOE
- Unexpected variation in replicate determinations
- Unexpected results in a Single Test or a Small Set of Tests
- What level of investigation is necessary and appropriate for OOE results?
Example Applications for Variables I - SPC
- Overview of the control of Continuous Data Monitoring for manufactured batches and for analytical test samples
- The basis for Statistical Process Control (SPC)
- Control Charts for Individuals
- Control Charts for Subgroups
- Control Charts for post mortem investigations
Example Applications for Variables II - Cusum for Investigations
- Importance of individuals and means
- Example of SPC for continuous individual data; a Moving Range (MR) Shewhart Chart
- Setting the control limits
- Example of SPC for continuous data for subgroups; Xbar and R
- Process Capability
- What if data are not normally distributed?
Recommended methods: Trending for Process Control of Attributes
- Theory and application of Cusum analysis
- Cusum versus EWMA charts
- Example of a post mortem Cusum investigation
Examples for Trending for Process Control of Attributes
- Basic differences between attributes and variables
- Control charts for attributes
- Applications for attribute data
Trending for Stability Data I; a simplified Linear Regression Approach
- Theory and application of n and np charts
- Theory and application of C and U charts
- Example of np charting
Trending for Stability Data II; a more advanced Random Coefficients Regression Model
- Challenges for trending stability data
- Simplified linear regression approach: assumptions and limitations, Minimum data requirements, Theory and calculation of prediction intervals
- Worked example illustrated using Excel
- Comparison with SAS JMP; why aren’t the numbers exactly the same?
- Why is it sometimes necessary?
- Basics of the RCR model
- Advantages and disadvantages over the simplified linear regression approach
- Evaluation of stability data
- Examples of its application using statistical packages
Workshop – Part I – Variables
Creating Control Charts in JMP
This Workshop will include a live construction of
variables control charts in SAS JMP v11
Dr Lance Smallshaw
Workshop – Part II – Attributes
This Workshop will include a live construction of
Attribute control charts in Minitab v18
Dr Chris Burgess
Workshop – Part III – Stability
Dr Lori A. McCaig
Workshop – Part IV – OOE
Based on real life examples, the delegates will learn a step-by-step approach to determine whether suspect results are really out of expectation or must be accepted as given variability of the method.
Dr Bernd Renger