05. November 2018 - 9:00
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Applied Statistics for Scientists and Engineers (NTZ) | Four Points by Sheraton Boston Logan Airport Revere | Monday, 05. November 2018

Applied Statistics for Scientists and Engineers


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.

Why should you attend:
21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries specify the application of statistical methods across the product quality lifecycle.
According to the Quality System Regulation (QSR) for medical devices, "Where appropriate, each manufacturer shall establish and maintain procedures for identifying valid statistical techniques required for establishing, controlling, verifying the acceptability of process capability and product characteristics." Although there are many statistical method that may be applied to satisfy this portion of the QSR, there are some commonly accepted methods that all companies can and should be using to develop acceptance criteria, to ensure accurate and precise measurement systems, to fully characterize manufacturing processes, to monitor and control process results and to select an appropriate number of samples.
According to both 21 CFR and guidance documents, the need for statistical methods is well established from discovery through product discontinuation. 21 CFR specifies the "the application of suitable statistical procedures" to establish both in-process and final specifications. The guidance documents necessitate the application of statistical methods for development and validation of measurement systems, process understanding using Quality by Design (QbD) principles, process validation, as well as ensuring the manufacturing process is in control and is capable.
This course provides instruction statistical methods for data analysis of applications related to the pharmaceutical, biopharmaceutical, and medical device industries.

Areas Covered in the Session:

describe and analyze the distribution of data
develop summary statistics
generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
describe the relationship between and among two or more factors or responses
understand issues related to sampling and calculate appropriate sample sizes
use statistical intervals to setting specifications/develop acceptance criteria
use Measurement Systems Analysis (MSA) to estimate variance associated with: repeatability, intermediate precision, and reproducibility
ensure your process is in (statistical) control and capable

Who Will Benefit:
This seminar is designed for pharmaceutical, biopharmaceutical, and medical device professionals who are involved with product and/or process design:

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

Day 1 ScheduleLecture 1:
Basic Statisticssample versus populationdescriptive statisticsdescribing a distribution of valuesLecture 2:
Intervalsconfidence intervalsprediction intervalstolerance intervalsLecture 3:
Hypothesis Testingintroducing hypothesis testingperforming means testsperforming normality tests and making non-normal data normalLecture 4:
ANOVAdefining analysis of variance and other terminologydiscussing assumptions and interpretationinterpreting hypothesis statements for ANOVAperforming one-way ANOVAperforming two-way ANOVA

Day 2 ScheduleLecture 1:
Regression and ANCOVAproducing scatterplots and performing correlationperforming simple linear regressionperforming multiple linear regressionperforming ANCOVAusing model diagnosticsLecture 2:
Applied Statisticssetting specificationsMeasurement Systems Analysis (MSA) for assaysstability analysisintroduction to design of experiments (DOE)process control and capabilitypresenting results


Jim Wisnowski
Jim Wisnowski is the cofounder of Adsurgo LLC and co-author of the book Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP. He has over 25 years of experience and currently provides training and consulting services to industry and government in Design of Experiments (DOE), Reliability Engineering, Data Visualization, Predictive Analytics, and Text Mining. Dr. Wisnowski has been an invited speaker on applicability of statistics for national and international conferences. Prior to his current position, he was a senior program manager for URS, Chief of the Statistics Division in the Mathematics Department at the Air Force Academy, and a retired military officer. He is currently a member of the editorial board of Quality Engineering and has published numerous international refereed journal articles on statistics. Jim has a PhD in Industrial Engineering from Arizona State University.

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