About this course
The Pharmaceutical Industry and the Regulatory Authorities are becoming increasingly aware of the benefits that statistical techniques can provide and the vital information that can be obtained by trend analysis. This is true in the context of ICH Q8, 9 and 10 and the 2011 FDA Guidance of Process Validation. This module is designed for delegates who have little or no experience in the application of statistical data analysis techniques.
The provision of useful information is essential to the Quality Leader and Technical Professional in helping them to:
- Understand the reliability and accuracy of data and information arising from samples taken from a population
- Monitor and detect adverse trends before a process goes out of specification
- Assess the capability and reliability of a process
- Understand the interaction of process parameters via experimental design
How This Training will Benefit You
Mathematics and Statistics represents a key element for the Quality Leader and the Technical Professional. This course provides the tools and techniques required to make informed, science and risk based decisions to benefit both the business and the patient.
It will provide the background and knowledge needed by every Quality Professional and Technical Leader whether in Production, Quality, R&D, Validation, Engineering, or any other technical discipline. As with all of NSF-DBA’s courses, it is led by industry and academic experts; frequently incorporating case studies and small group sessions to facilitate learning.
NSF-DBA’s format of extended days and pre-work ensures that both you and your company benefit from the learning experience in an efficient way and with minimal time away from the workplace.
This course on Mathematics and Statistics is based upon a key component of the Qualified Persons instruction which NSF-DBA offers in Europe. It sets a new standard for practical, results oriented instruction.
A Certificate is granted by NSF-DBA for attendance. Higher degree options – a Diploma and a Masters in Pharmaceutical Quality and Good Manufacturing Practice from the University of Strathclyde can be obtained by attending a full series of courses.
Continuing Education Credit
ACPE Continuing Education Units = 2.4 CEUs
Reference Activity Number 0616-9999-12-002-L04-P
Course outline
Basic Statistical Quantities
- Terminology and definitions
- Measures and visual plots to describe populations and samples
- Deciding how much data to collect to enable appropriate decision making to occur
Statistical Techniques
- ISO 2859 – Specification for sampling procedures and tables, application of sampling by attributes
- Shewhart and Cusum control charts
- Process Capability (Cp and CpK)
- Control Charts
- Outlier testing
- Regression analysis
- The statistical test
- Analysis of Variation (ANOVA)
- Experimental design e.g. Taguchi
- Multi-variate analysis
Practical Application of Statistical Techniques
- Practical examples of the application of statistical techniques using teamwork sessions
- Experience of the application of statistical techniques within a manufacturing environment; i.e. for Statistical Process Control (SPC)
- Six Sigma: What is it and what are its benefits
Discussion and Working Groups
A significant proportion of the course time will be devoted to group work, where delegates have the opportunity, through case studies, to put theory into practice.
Additionally, discussion periods, provide delegates with an opportunity to obtain answers to their specific questions and concerns.
Venue
Royal Sonesta Hotel Boston, MA, USA
The Royal Sonesta hotel is minutes from downtown Boston. Located
along the Charles River, it is close to many of the Boston historic sites,
including the Boston Science Museum. There is excellent shopping
nearby.
Hotel Accommodation
NSF-DBA has negotiated a preferred rate with the Royal Sonesta
Hotel. A limited number of rooms are available at this rate. Hotel
reservations and payment are the responsibility of each delegate.
We will be happy to help but final confirmation and bill settlement
is the delegate’s responsibility.
There are a number of other hotels located within minutes from the
Royal Sonesta Hotel including the Marriott Hotel, the Courtyard Inn
and Le Meridien Hotel, all situated in Cambridge. Downtown Boston
hotels are within 15 minutes by taxi or public transportation of the meeting venue.
Royal Sonesta Hotel, 40 Edwin Land Blvd., Cambridge, MA 02142, Tel: 617-806-4200.
Tutors
We believe that our team of experts are the best available. Not only have they got years of experience in the industry, but they are committed to tutoring and helping those inside the industry to improve and raise the standards. We understand that you attend courses to learn about the latest regulations and to get your questions answered.
Our experts for this course are:
Professor George Gettinby, Dept of Statistics and Modelling Science, University of Strathclyde, Glasgow, UK
Consultant Data Analyst to the pharmaceutical industry, governing bodies and international agencies.
Karthik Iyer, Guest Speaker
Senior Policy Advisor/Statistician at CDER, FDA (ASQ certified CSSBB, CQE) in the risk science team that supports the drug manufacturing and quality office enforcement of current good manufacturing practices per 21 CFR 210 and 211.
Please note that Karthik’s participation in this course as a guest lecturer only reflects the views of the author and does not represent the views of the Agency or the United States government
What you've said
“I really enjoyed the instructors. They were able to liven up the topic of statistics and really presented it in an applicable way.”
Audrey Schupp, Lundbeck Inc, USA
“Finally some light bulbs for statistics at last! After 30 plus years of not getting it.”
Sandra Ahern, Genzyme, USA
“I am very surprised at how much I enjoyed this class. The tutors presented it very well and made it interesting and easy to follow.”
Ann Farnsworth, Merrimack Pharmaceuticals, USA
“Absolutely loved it! I can’t wait to get back to work and introduce the ‘ever-feared’ statistics into what I do.”
Emily Norman, Schwarz Pharma Manufacturing Inc, USA
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