Avoiding Statistical Pitfalls during Method Validation - Webinar By GlobalCompliancePanel

Website http://alturl.com/azfm3 | Edit Freely

Category Statistical Pitfalls,Method Validation,Quality assurance,Precision,Accuracy.

Deadline: July 24, 2012 | Date: July 24, 2012

Venue/Country: Wilmington, U.S.A

Updated: 2012-07-02 21:31:28 (GMT+9)

Call For Papers - CFP

Overview: The ICH guideline on Validation of Analytical Procedures (Q2R1) delineates the guidance and methodology for validation characteristics of an analytical procedure.

Like most guidelines, the terminology is vague enough to allow for several acceptable approaches and analysis. Appropriate statistical methods should be used and all relevant data collected during validation and formulae used for calculating validation characteristics should be submitted and discussed as appropriate. A statistical approach to validation of analytical methods can minimize the amount of testing while meeting the requirements of the guideline.

Areas Covered in the Session:

Introduction to ICH Q2R1

Statistical methods used during validation

Accuracy versus Precision

Examples and case studies

Who Will Benefit:

Anyone responsible for, or affected by, FDA regulations on analytical method validation

Management

Research and Development

Regulatory Affairs personnel

Quality assurance/quality control personnel

Auditors and inspectors

Mr. Steven Walfish is the President of Statistical Outsourcing Services, a consulting company that provides statistical analysis and training to FDA regulated industries. Prior to starting Statistical Outsourcing Services, Mr. Walfish was the Senior Manager Biostatistics, Non-clinical at Human Genome Sciences in Rockville MD. Prior to joining HGS, Mr. Walfish was a Senior Associate at PricewaterhouseCoopers specializing in the pharmaceutical industry. Mr. Walfish was Manager of Non-Clinical Statistics at Chiron Diagnostics. Mr. Walfish brings over 20 years of industrial expertise in the development and application of statistical methods for solving complex business issues including data collection, analysis and reporting.