Training Course
Syllabus:
Sample Size Justification & Statistical Analysis for Performance Qualification (PQ) Studies
Course Description Regulatory agencies mandate formal protocols for Performance Qualification (PQ) studies to determine whether products/processes meet the desired requirements. When planning PQ studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered. This 2-day seminar will provide a 12-step process to assist you in writing/reviewing protocols for PQ studies with a focus on sample size justification, acceptance criteria and statistical analysis using Minitab v17. Validation of software will not be covered. Course Information: Participants are requested to bring a laptop with Minitab Version 17 software installed. Learning Objectives:
Upon completing this course participants will be able to: Determine a Representative Sample for Process Qualification (PQ) study. Develop product/process acceptance criteria statements Determine appropriate Sample Size to meet PQ acceptance criteria Understand how to Analyze PQ data Understand how to calculate and interpret statistical tolerance limits Determine and verify the appropriate distribution of the data Interpret probability plots Apply normalizing transformations Handle censored data and mixed failure modes. DAY 01(8:30 AM - 4:30 PM) Registration Process - (8:30 am till 8:45 am) Lecture 1: Introduction Course Objectives Selecting Representative Sample Confidence Level Definition General Flow Chart for 12-step Protocol Development Lecture 2: Sample Size Determination for Performance Qualification Study General Purpose Acceptance Criteria Attribute (Pass/Fail) Response Censored (Variable) Response Lecture 3: Non-Censored (Variables) Response Tolerance Limits Defined One-Sided vs Two-Sided Limits Normality Assumptions Nonparametric Tolerance Limits DAY 02(8:30 AM - 4:30 PM) Lecture 4: Analyzing PQ Data using Minitab Statistical Tolerance Limits Confidence Level Percentage of Population captured Lecture 5: Distribution Fitting using Graphical Methods Histograms Boxplots Probability Plots Lecture 6: Statistical Tests for Distribution Fit Anderson-Darling Ryan-Joiner Kolmogorov-Smirnov Lecture 7: Normalizing Transformations Lognormal 3-Parameter Lognormal Box-Cox Johnson Lecture 8: “Non-Normal” Statistical Tolerance Limits Weibull 3-Parameter Weibull Smallest Extreme Value Mixed populations or multiple failure modes |