BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20240329T160036Z UID:ce826ece-15b4-4d9f-a92a-70415d81dd38 DTSTART:20211105T100000 DTEND:20211106T100000 CLASS:PRIVATE DESCRIPTION:
Throughout 21 CFR and guidance documents for the pharmaceutical\, biopharmaceutical\, and medical device industries\, the application of st atistical methods are specified for: setting validation criteria and speci fications\, performing measurement systems analysis (MSA)\, conducting sta bility analysis\, using design of experiment (DOE) for process development and validation\, developing process control charts\, and determining proc ess capability indices.
\n\nDifferent statistical methods are requir ed 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 hy pothesis tests\, analysis-of-variance (ANOVA)\, regression\, and ANCOVA ar e methods used for analyzing designed experiment for process development a nd validation studies. Descriptive statistics (distribution\, summary stat istics)\, run charts\, and probability (distributions) are used for develo ping process control charts and developing process capability indices.
\n\nThis course provides instruction on how to apply the appropriate st atistical approaches: descriptive statistics\, data intervals\, hypothesis testing\, ANOVA\, regression\, ANCOVA\, and model building. Once competen ce in each of these areas is established\, industry-specific applications are presented for the participants.
\n\n21 CFR and guidance documents for the pharmaceutical\, biop harmaceutical\, and medical device industries specify the application of s tatistical methods across the product quality lifecycle.
\n\nAccordi ng to the Quality System Regulation (QSR) for medical devices\, "\;Whe re appropriate\, each manufacturer shall establish and maintain procedures for identifying valid statistical techniques required for establishing\, controlling\, verifying the acceptability of process capability and produc t characteristics."\; Although there are many statistical method that may be applied to satisfy this portion of the QSR\, there are some commonl y accepted methods that all companies can and should be using to develop a cceptance criteria\, to ensure accurate and precise measurement systems\, to fully characterize manufacturing processes\, to monitor and control pro cess results and to select an appropriate number of samples.
\n\nAcc ording to both 21 CFR and guidance documents\, the need for statistical me thods is well established from discovery through product discontinuation. 21 CFR specifies the "\;the application of suitable statistical proced ures"\; to establish both in-process and final specifications. The gui dance documents necessitate the application of statistical methods for dev elopment and validation of measurement systems\, process understanding usi ng Quality by Design (QbD) principles\, process validation\, as well as en suring the manufacturing process is in control and is capable.
\n\nT his course provides instruction statistical methods for data analysis of a pplications related to the pharmaceutical\, biopharmaceutical\, and medica l device industries.
\n\nThis seminar is designed for pharmaceutical\, biopharma ceutical\, and medical device professionals who are involved with product and/or process design:
\n\nJim Wisnowski \;is the cofounder
of Adsurgo LLC and co-author of the book Design and Analysis of Experiment
s by Douglas Montgomery: A Supplement for using JMP. He has over 25 years
of experience and currently provides training and consulting services to i
ndustry and government in Design of Experiments (DOE)\, Reliability Engine
ering\, Data Visualization\, Predictive Analytics\, and Text Mining. Dr. W
isnowski has been an invited speaker on applicability of statistics for na
tional and international conferences. Prior to his current position\, he w
as a senior program manager for URS\, Chief of the Statistics Division in
the Mathematics Department at the Air Force Academy\, and a retired milita
ry officer. He is currently a member of the editorial board of Quality Eng
ineering and has published numerous international refereed journal article
s on statistics. Jim has a PhD in Industrial Engineering from Arizona Stat
e University.
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