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Adsurgo has training available in virtually all disciplines of applied statistics and analytics. We can tailor a course of any length for on-site delivery or via web-ex. Where possible, we use customer examples and data sets to better connect material to job function.
Event Date Description
Introduction to JMP (0.5 days)
  • February 2, 2022 12:00 pm

Introduction to JMP (1.0 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:

  • import and arrange data for analysis including JMP data table productivity features
  • perform data exploration
  • create summaries, tables, charts, and graphs
  • utilize Graph Builder


1. Introduction to JMP

  • navigating JMP
  • importing data tables in JMP
  • column menu
  • rows menu
  • creating formulas

2. Graphical Analysis

  • producing univariate graphs
  • producing bivariate and multivariate graphs
  • time series and maps
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Data Visualization and Storytelling (1.0 days)
  • February 8, 2022 12:00 pm
  • March 22, 2022 12:00 pm

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:

  • Understand data visualization principals
  • Know the characteristics of data that help guide appropriate visualizations
  • Be able to apply a disciplined process for designing good graphics and dashboards
  • Create graphs for different data types to include univariate, multivariate, categorical, text, time series, geographic, and modeling output.
  • Be able to tell the story of applied statistical analysis integrating the narrative with appropriate displays

• Introduction, definitions, and historical perspectives
• Examples of data visualizations and interactivity
• Characteristics of data

Designing Effective Graphics
• Human side of data visualization
• Principles of good graphic design
• Data visualization methodology and best practices
• Dashboards

Creating the Right Graph
• Univariate plots: distributions, histograms, and boxplots
• Multivariate plots: scatterplots, contour plots, treemaps
• Categorical data plots: pie/doughnut graphs, mosaic plots
• Text/Unstructured data: word clouds, topic plots, word and documents clusters
• Time series data: trend lines/forecasts, waterfall plots, sparklines
• Geographic data: maps, custom maps

Graphics from Modeling
• Model adequacy checks
• Factor profilers and optimization
• Comparing competing models

Telling the story
• Data storytelling steps and principals
• Best practices
• Oral presentation tips

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Applied Statistics for Scientists (2.0 days)
  • February 14, 2022 12:00 pm

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:

  • describe and analyze the distribution of data
  • develop summary statistics
  • generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
  • understand issues related to sampling and calculate appropriate sample sizes
  • describe the relationship between and among two or more factors or responses


1. Descriptive Statistics
• introducing summary statistics
• describing a distribution of values
• creating a summary table and tabulating data

2. Intervals
• producing confidence intervals
• producing tolerance intervals

3. Hypothesis Testing
• introducing hypothesis testing
• performing means (and variance) tests
• performing proportion tests

• defining analysis of variance and other terminology
• discussing assumptions and interpretation
• interpreting hypothesis statements for ANOVA
• performing one-way ANOVA

5. Simple Linear Regression
• producing scatterplots and performing correlation
• performing simple linear regression

6. Model Building
• introducing model building
• performing N-way ANOVA
• performing multiple linear regression
• performing ANCOVA

7. Model Diagnostics
• evaluating the validity of model assumptions
• evaluating model fit

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Design of Experiments (DOE) using JMP (1.0 days)
  • February 22, 2022 12:00 pm
  • March 29, 2022 12:00 pm

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:

  • identify critical quality attributes (CQAs) that will be used as responses in your designs
  • utilize risk management tools to identify and prioritize potential critical process parameters
  • identify critical process parameters and develop a functional relationship between those process
    parameters and your critical-to-quality attributes (CQAs) using both screening and response surface
  • be able to design and analyze screening designs including a factorial, fractional factorial, and D-optimal
  • understand the need for adding center points to a design
  • be able to design and analyze response surface designs including central composite designs (CCDs),
    Box-Behnken designs, and I-optimal designs
  • be able to design and analyze definitive screening designs
  • present results of DOE studies



  1. Introduction to Design of Experiments (DOE)
    • steps to DOE
    • defining critical-to-quality attributes (CQAs)
    • identifying and prioritizing potential process parameters
  2. Screening Designs – Identifying Critical Process Parameters
    • factorial designs
    • fractional factorial designs
    • D-optimal designs
  3. Response Surface Designs – Develop Functional Relationships and Establish Design Space
    • Central Composite Designs (CCDs)
    • Box-Behnken designs (Self Study)
    • I-optimal designs
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Demonstration of Comparability for Chemistry, Manufacturing, and Controls (CMC) in the Pharmaceutical Industry (0.5 days)
  • March 2, 2022 12:00 pm

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:

  • Explanation of differences between statistical methods used for demonstrating comparability and when each is appropriate.
  • A list of considerations for selecting an appropriate method.
  • An approach for setting comparability criteria.
  • A method to determine reasonable sample sizes.

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).


  1. Comparability Studies
    • Comparability Basics and Definitions
    • Comparability Tools and Statistical Designs
    • CMC Applications
  2. Comparability Tools
    • Considerations in Selecting an Approach
    • Statistical Tests
    • Quality Ranges
    • Comparison of Methods
  3. Defining Acceptance Criteria and Power Calculations
    • Power and Statistical Power Calculations
    • Final Recommendations
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Advanced Design of Experiments (1.0) Days
  • March 8, 2022 12:00 pm
  • April 5, 2022 12:00 pm

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:

  • Understand the impact of power, prediction variance, optimality criterion (A, D, or I) and correlation/alias structure in designs
  • Create custom designs in the presence of a constrained factor space
  • Augment designs with additional runs taking advantage of what you learned from those tests already conducted.
  • Know when to use and be able to construct split-plot designs, supersaturated designs, space filling designs, and mixture designs


  1. DOE review
    • Classical designs
    • Metrics of a good design
    • Custom designs and optimality criterion
  2. Designs Methods
    • Constrained designs and disallowed combinations
    • Augmenting designs
  3. Advanced Designs
    • split-plot designs
    • supersaturated designs
    • space filling designs
    • mixture designs
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Spec Setting and Stability Analysis using JMP (0.5 days)
  • March 11, 2022 12:00 pm

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:

  • know the recommended approaches for setting specifications
  • be able to calculate the range, a reference interval, and tolerance interval
  • understand how to adjust a reference interval to account for small sample sizes
  • understand the purpose of a stability study
  • know the need to evaluate stability dat
  • understand the importance for a test of poolability of batches
  • be able to use Analysis of Covariance (ANCOVA) is used to evaluate stability data
  • be able to use JMP to determine expiries based upon the criteria outlined in the ICH guidance on
    evaluation of stability data
  • understand the need for accelerated degradation analysis
  • be able to fit a nonlinear function using the Arrhenius acceleration factor


1. Setting Specifications
• Min, Max Intervals
• Reference Intervals – Normal Distribution
• Reference Intervals – Non-Normal Distribution (Self Study)
• Tolerance Interval
• Adjusted Reference Intervals for Small Sample Sizes

2. Stability Analysis
• Purpose of stability analysis
• Setting expiries – Situation 1
• Setting expiries – Situation 2
• Setting expiries – Situation 3
• Evaluating accelerated degradation analysis using the Arrhenius acceleration factor
• Evaluating stability for non-linear degradation (self study)

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Reliability Engineering and Survival Analysis (1.0 days)
  • March 15, 2022 12:00 pm

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:

  • Understand why and how to censor data from survival studies
  • Be able to interpret nonparametric survival plots and models
  • Determine the best probability distribution and parameter estimates to characterize reliability
  • Be able to model the impact of predictor variables (covariates)
  • Calculate sample size estimates for reliability demonstrations
  • Model system performance with survival and parametric survival techniques

1. Introduction to Reliability Methods
• Censoring and event plots
• Functions and definitions: reliability, survival, hazard, cumulative
• Nonparametric survival methods
• Probability distributions for reliability analysis

2. Reliability and Survivability Models
• Survival analysis
• Reliability with a single predictor variable
• Reliability with multiple predictor variables using parametric survival models

3. Advanced Approaches
• Degradation and stability analysis
• Sample size determination
• Accelerated life tests

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Quality by Design for the Life Cycle of an Analytical Procedure (2.0 days)
  • April 11, 2022 12:00 pm

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

  • Apply a Quality by Design (QbD) approach throughout the lifecycle of an analytical procedure.
  • Reference legal and regulatory documents reference requirements for analytical validation.
  • Describe the principles of analytical validation referenced by those documents.
  • Set analytical procedure validation requirements that are fit for their intended use.
  • Apply the statistical tools for QbD throughout the lifecycle of an analytical procedure.


  1. Introduction to AQbD
    • AQbD principles
  2. Primer on Statistical Analysis
    • Basic Statistics
    • Statistical intervals and hypothesis testing
    • ANOVA and regression
    • Process control charts and capability
  3. USP <1225> and ICH Q2
  4. Stage 1: Procedure Design
    • Defining fitness for intended use
    • Design considerations
  5. Stage 2: Procedure Performance Qualification (PPQ)
    • Analysis – FUV CD
    • Analysis – AUC
  6. Stage 3: Continued Verification
    • System suitability and analytical control.
    • Continued verification.
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