Statistical Methods Across the Product/ Process Lifecycle

Date to be announced

Price available after dates are announced

TRAINING TIMES

TBDVienna
TBDLondon
TBDNew York
TBDLos Angeles

LEARNING OBJECTIVES

The most common and powerful statistical methods and their primary sue across the product lifecycle

The relationship of statistical methods and Quality Risk Management (QRM)

Key reasons to use Deign of Experiments (DOE) for process development

Modelling populations to assure quality

Difference between hypothesis testing and equivalence testing for comparability

Understanding the components of a sampling plan and associated acceptance criteria for Process Validation batches

Distinguish between intra- and inter- batch variability and methods to assess each

Elements and nuances of an Ongoing Process Verification (OPV) Program

Combination of root cause analysis with data visualization to speed root cause identification

KEY TOPICS

Integrating data analytics to accelerate development, improve processes and reduce risk

Ensure Quality by Design and Optimal Control Strategy

Sampling and Acceptance Criteria

TRAINER

Tara Scherder

SynoloStats, USA, Principal

Tara Scherder has over 20 years’ experience in the pharmaceutical and biopharmaceutical industries as a statistician, engineer, and master black belt. Her passion is the powerful combination of science and statistics to increase knowledge, optimize both manufacturing and business processes, and reduce risk. She combines statistical expertise with extensive knowledge of manufacturing platforms/analytical sciences and CMC requirements, always within business and regulatory context to achieve goals. She frequently speaks at industry forums and publishes on the practical incorporation of statistical methods for Lifecycle Process Validation. Tara earned a BS degree in Chemical Engineering from the University of Pittsburgh and a MS degree in Statistics from Carnegie Mellon University.

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