Throughout 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries, the application of statistical methods are specified for: setting validation criteria and specifications, performing measurement systems analysis (MSA), conducting stability analysis, using design of experiment (DOE) for process development and validation, developing process control charts, and determining process capability indices.
Different statistical methods are required for each of these particular applications. Data and tolerance intervals are common tools used for setting acceptance criteria and specifications. Simple linear regression and analysis-of-covariance (ANCOVA) are used for setting expiries and conducting stability analysis studies. Two-sample hypothesis tests, analysis-of-variance (ANOVA), regression, and ANCOVA are methods used for analyzing designed experiment for process development and validation studies. Descriptive statistics (distribution, summary statistics), run charts, and probability (distributions) are used for developing process control charts and developing process capability indices.
This course provides instruction on how to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building. Once competence in each of these areas is established, industry-specific applications are presented for the participants.
Day 1 Schedule
Lecture 1: Basic Statistics
- Sample versus population
- Descriptive statistics
- Describing a distribution of values
Lecture 2: Intervals
- Confidence intervals
- Prediction intervals
- Tolerance intervals
Lecture 3: Hypothesis Testing
- Introducing hypothesis testing
- Performing means tests
- Performing normality tests and making non-normal data normal
Lecture 4: ANOVA
- Defining analysis of variance and other terminology
- Discussing assumptions and interpretation
- Interpreting hypothesis statements for ANOVA
- Performing one-way ANOVA
- Performing two-way ANOVA
Day 2 Schedule
Lecture 1: Regression and ANCOVA
- Producing scatterplots and performing correlation
- Performing simple linear regression
- Performing multiple linear regression
- Performing ANCOVA
- Using model diagnostics
Lecture 2: Applied Statistics
- Setting specifications
- Measurement Systems Analysis (MSA) for assays
- Stability analysis
- Introduction to design of experiments (DOE)
- Process control and capability
- Presenting results
Richard K. Burdick,
Emeritus Professor of Statistics ,
Arizona State University
Richard (Rick) K. Burdick is an Emeritus Professor of Statistics, Arizona State University (ASU) and former Quality Engineering Director for Amgen, Inc. for 10 years. He taught at ASU for 29 years at all levels including undergraduate business students, MBAs, Master of Statistics students, and doctoral candidates in both business and engineering. He received numerous teaching awards and taught a variety of courses for adult learners. His research and consulting interests consider several CMC statistical applications including comparability studies, stability data analysis, analytical method validation, quality by design process characterization, and analytical similarity for biosimilar products. He has written over 60 journal articles and three books, including Confidence Intervals for Random and Mixed ANOVA Models with Applications to Gauge R&R Studies, (with C. M. Borror and D. C. Montgomery) and Confidence Intervals on Variance Components, (with F. A. Graybill). Burdick is a Fellow of the American Statistical Association and a member of the American Society for Quality. He has served on the USP Statistics Expert Committee since 2010. He received his Bachelor's Degree in Statistics from the University of Wyoming. He received his Masters and Doctorate degrees in Statistics from Texas A&M University.
- Process Scientist/Engineer
- Design Engineer
- Product Development Engineer
- Regulatory/Compliance Professional
- Design Controls Engineer
- Six Sigma Green Belt
- Six Sigma Black Belt
- Continuous Improvement Manager
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