JMP Training
Getting Started with JMP
Design of Experiments
Advanced JMP & JMP Pro
JMP Scripting Language (JSL)
Classic JMP Courses
Other Analytics Courses & Training
Event | Date | Description | |
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Design of Experiments (DOE) using JMP (1.0 days) |
| DOE using JMP (1.0 days): This course focuses on how to establish a systematic approach to pharmaceutical development using design of experiments (DOE). All concepts are taught within the product quality system framework. Design of Experiments (DOE) using JMP:
Outline:
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Introduction to JMP (0.5 days) |
| Introduction to JMP (0.5 days): The course provides an introduction on how to use JMP statistical software with an emphasis on importing data, summary tables, charts, and graphs. Introduction to JMP:
Outline:1. Introduction to JMP
2. Graphical Analysis
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What's New in JMP 18....and JMP 17! |
| During a recent What’s New in JMP 18 seminar, the customers kept asking about other new JMP features. These other new features were new, but they were new in JMP 17! JMP updates software so frequently, many customers don’t have time to learn new features of one version before the next version is out. This complementary seminar will benefit those customers that are still using JMP 17 as well as those that have updated to JMP 18. Thursday August 1, 2024 from 1:00 – 3:00 Eastern Time |
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Quality by Design (QbD) using DOE (3.0 days) |
| Quality by Design (QbD) using DOE (3.0 days): This course focuses on This course focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance Quality by Design (QbD) using DOE Objectives:
Outline 1. Introduction to Quality by Design (QbD) 2. Primer on Statistical Analysis 3. Foundational Requirements for QbD Studies 4. Introduction to Design of Experiments (DOE) 5. Screening Designs – Identifying Critical Process Parameters 6. Response Surface Designs – Develop Functional Relationships and Establish Design Space 7. Specialized Designs 8. Utilizing Systematic Understanding from QbD Studies 9. Advanced DOE Methodologies (Self Study) |
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Advanced Design of Experiments (1.0) Days |
| JMP Advanced DOE (1.0 days): This course builds on the DOE Using JMP material by providing more insight into the metrics of good designs such as prediction variance, power analysis, design optimality, and correlation between factors. We build on custom designs with constrained design regions/disallowed combinations and augmenting existing designs with new runs. We introduce new classes of designs useful in many practical applications: split plot designs (hard-to-change factors), space filling designs, mixture designs, and supersaturated (more factors than runs) designs. JMP Advanced DOE Objectives:
Outline:
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Quality by Design for the Life Cycle of an Analytical Procedure (2.0 days) |
| Quality by Design for the Life Cycle of an Analytical Procedure (2.0 days): The pharmaceutical industry relies on analytical procedures for the combined practices of process and formulation development, process validation, and manufacturing, as well as both in-process and final-product testing. These test methods need not only be validated but reliable and fit for its intended use. This course provides a systematic approach to the lifecycle of analytical procedures that begins in procedure design and development, continues in procedure performance qualification, and is maintained in continued procedure performance verification. Quality by Design for the Life Cycle of an Analytical Procedure
Outline:
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Data Visualization and Storytelling (1.0 days) |
| Data Visualization and Storytelling (1.0 days): This course will provide principals to effectively communicate your technical results professionally. The methods will allow you to interactively discover deeper relationships graphically more efficiently. We will provide the foundations for creating better graphical information to accelerate the insight discovery process and enhance the understandability of reported results. First principles and the “human as part of the system” aspects of information visualization from multiple leading sources are explored using representative univariate, multivariate, time series, geographic, and unstructured text data sets. Data Visualization and Storytelling Objectives:
Introduction Designing Effective Graphics Creating the Right Graph Graphics from Modeling Telling the story |
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Applied Statistics for Scientists (2.0 days) |
| Applied Statistics for Scientists (2.0 days): This course provides instruction on how to apply scientific approaches to data analysis by focusing on appropriate methods for analysis: descriptive statistics, hypothesis testing, analysis of variance and model building. Applied Statistics for Scientists Objectives:
Outline: 1. Descriptive Statistics 2. Intervals 3. Hypothesis Testing 4. ANOVA 5. Simple Linear Regression 6. Model Building 7. Model Diagnostics |
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Demonstration of Comparability for Chemistry, Manufacturing, and Controls (CMC) in the Pharmaceutical Industry (0.5 days) |
| Demonstration of Comparability for Chemistry, Manufacturing, and Controls (CMC) in the Pharmaceutical Industry (0.5 days): The FDA comparability guidance (1996) recognizes the need for manufacturers to improve manufacturing processes and analytical procedures without performing additional clinical studies to demonstrate product safety and efficacy. This guidance was extended in ICH Q5E (2004) to provide additional direction for comparing pre- and post-change manufacturing processes.
Across the regulatory documents, there are only high-level recommendations for the design of an equivalence/comparability study and for setting acceptance criteria to assess the impact of the change. These documents do not generally contain prescriptive rules for setting acceptance criteria, study design, or statistical procedures for analysis. One notable exception is the draft guidance published by FDA (2019) concerning demonstration of analytical similarity. EMA also produced a report based on comments offered on a reflection paper concerning analytical comparability and similarity (2018). This course presents statistical approaches for demonstrating comparability consistent with these regulatory guidelines. In addition, this course provides JMP scripts for power and sample size determinations as well as appropriate constants for intervals used to calculate quality ranges: Demonstration of Comparability for CMC in the Pharmaceutical Industry:
Examples discussed in the course include: qualification of reference standards, comparison of manufacturing sites, partitioning of variation in gene and cell therapies, analytical bridging studies, tech and analytical transfer, scale-down model (SDM) qualification, and analytical similarity of biosimilars. Methods discussed in the course include: side-by-side plots, statistical equivalence tests for means (TOST), non-inferiority of standard deviations, and quality ranges (min-max intervals, tolerance intervals, 3-sigma intervals, and risk-based side-by-side intervals). Outline:
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Statistics for FDA Process Validation (3.0 days): |
| Statistics for FDA Process Validation (3.0 days): This course focuses on how to establish a systematic approach to implementing statistical methodologies into a process development and validation program consistent with the FDA guidance. This course teaches the application of statistics for setting specifications and assessing measurement systems (assays), using design of experiments (DOE) for process design, establishing a strategy for process qualification, and ensuring process control/capability. All concepts are taught within the three-stage product cycle framework defined by requirements in the process validation regulatory guidance documents. Statistics for FDA Process Validation Objectives:
Stage 1 – Process Design Stage 2 – Process Qualification Stage 3 – Continued Process Verification Outline: 2. Primer on Statistical Analysis 3. Foundational Requirements for Process Validation 4. Stage 1 – Process Design 5. Stage 2 – Process Qualification 6. Stage 3 – Continued Process Verification 7. Advanced Methodologies (Optional) Appendix A – Capstone Exercise |
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Statistical Tools for Process Control and Capability Analysis (0.5 days) |
| Statistical Tools for Process Control and Capability Analysis (0.5 days):
This course provides instruction on how to apply statistical tools to ensure your process operates in a state of (statistical) control. Although multiple tools are demonstrated, the course focuses on specific tools associated with SPC and process capability. All concepts are taught within the quality system framework. Statistical Tools for Process Control and Capability Analysis Objectives:
Outline: 1. Primer on Statistical Analysis for Process Control and Capability 2. Statistical Process Control and Capability |
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Reliability Engineering and Survival Analysis (1.0 days) |
| Statistical Methods for Reliability Engineering and Survival Analyses: This course covers the graphical and quantitative methods used reliability and survival analysis using JMP software. We will explore statistical methods to characterize and predict reliability and probability of survival. Topics include censoring, probability distributions—particularly Weibull and Lognormal, sample size determination, degradation/stability analysis, reliability with covariates/predictor variables, survival analysis, and fitting parametric survival models. Statistical Methods for Reliability Engineering and Survival Analyses Objectives:
1. Introduction to Reliability Methods 2. Reliability and Survivability Models 3. Advanced Approaches |
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Spec Setting and Stability Analysis using JMP (0.5 days) |
| Spec Setting and Stability Analysis using JMP (0.5 days): This course provides instruction on how to use JMP statistical software for setting specifications and stability analysis including degradation analysis using the Arrhenius acceleration factor. Spec Setting and Stability Analysis using JMP Objectives:
Outline: 1. Setting Specifications 2. Stability Analysis |
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