Expertise & Capabilities

Adsurgo is a professional services company solving problems through direct engagement consulting services and training workshops focused on the use of analytics.

ANALYTICS COURSES

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Event Date Description
Medtronic #3
  • July 15, 2022 8:00 am
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Applied Statistics for Scientists (2.0 days)
  • August 25, 2022 9:00 am

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

Outline:

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

4. ANOVA
• 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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Quality by Design (QbD) using DOE (3.0 days)
  • September 13, 2022 9:00 am

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
documents. Analyses in this course use the point-and-click interface of JMP Software by SAS.

Quality by Design (QbD) using DOE Objectives:

  • implement QbD principles from discovery through product discontinuation
  • apply statistics to set specifications and validate measurement systems (assays)
  • 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)
  • establish your design space
  • develop a control plan as part of a risk management strategy
  • ensure your process is in (statistical) control and capable.

Outline

1. Introduction to Quality by Design (QbD)
• Quality by Design (QbD) principles
• Product Quality System framework

2. Primer on Statistical Analysis
• basic statistics
• hypothesis testing
• ANOVA and regression
• Blocking

3. Foundational Requirements for QbD Studies
• setting specifications
• Measurement Systems Analysis (MSA) for assays

4. Introduction to Design of Experiments (DOE)
• steps to DOE
• defining critical-to-quality attributes (CQAs)
• identifying and prioritizing potential process parameters

5. Screening Designs – Identifying Critical Process Parameters
• factorial designs
• fractional factorial designs
• D-optimal designs

6. Response Surface Designs – Develop Functional Relationships and Establish Design Space
• Central Composite Designs (CCDs)
• Box-Behnken designs (Self Study)
• I-optimal designs

7. Specialized Designs
• Definitive Screening Designs
• A Optimal Designs

8. Utilizing Systematic Understanding from QbD Studies
• presenting results
• developing a control plan as part of a risk management strategy
• process control and capability

9. Advanced DOE Methodologies (Self Study)
• split-plot designs
• robust parameter design
• supersaturated designs
• constrained designs
• mixture designs

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Reliability Engineering and Survival Analysis (1.0 days)
  • September 14, 2022 9:00 am

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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Statistics for FDA Process Validation (3.0 days):
  • September 19, 2022 9:00 am

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:

  • apply statistics to set specifications and validate measurement systems (assays)
  • develop appropriate sample plans based upon confidence and power
  • implement suitable statistical methods into a process validation program for each stage:

Stage 1 – Process Design
◦ utilize risk management tools to identify and prioritize potential critical process parameters
◦ define critical process parameters and operating spaces for the commercial manufacturing process using design of experiments (DOE).

Stage 2 – Process Qualification
◦ assess scale effects while incorporating large (pilot) scale data
◦ develop process performance qualification (PPQ) acceptance criteria by characterizing intra and inter-batch variability using process design data and batch homogeneity studies
◦ develop an appropriate sampling plan for PPQ.

Stage 3 – Continued Process Verification
◦ develop a control plan as part of a risk management strategy
◦ collect and analyze product and process data
◦ ensure your process is in (statistical) control and capable.

Outline:
1. Introduction to Statistics for Process Validation
• Process Validation principles
• Stages of Process Validation

2. Primer on Statistical Analysis
• Basic statistics
• Statistical intervals and hypothesis testing
• ANOVA and regression
• Time series charts

3. Foundational Requirements for Process Validation
• Setting specifications
• Analytical methodology
• Sampling plans

4. Stage 1 – Process Design
• Steps to DOE
• Defining critical-to-quality attributes (CQAs)
• Identifying and prioritizing potential process parameters
• Screening designs
• Response surface designs
• Establishing a strategy for process control

5. Stage 2 – Process Qualification
• Batch homogeneity
• Setting PPQ acceptance criteria
• Scale effects
• Characterizing inter and intra-batch variability

6. Stage 3 – Continued Process Verification
• Developing a control plan as part of a risk management strategy
• Statistical process control
• Process capability

7. Advanced Methodologies (Optional)
• Advanced DOE
• Advanced Process Control
• Advanced Process Capability

Appendix A – Capstone Exercise
Appendix B – Reference Material

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Design of Experiments (DOE) using JMP (1.0 days)
  • September 26, 2022 9:00 am
  • October 24, 2022 9:00 am

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
    designs
  • be able to design and analyze screening designs including a factorial, fractional factorial, and D-optimal
    design
  • 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

 

Outline:

  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

Other Design of Experiments Classes and Training

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Advanced Design of Experiments (1.0) Days
  • September 27, 2022 9:00 am
  • October 26, 2022 9:00 am

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

Outline:

  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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Statistical Tools for Process Control and Capability Analysis (0.5 days)
  • September 28, 2022 1:00 pm
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:

  • Be able to use tools to ensure your process is in (statistical) control and capable
  • Generate and analyze a distribution and run chart of time series data
  • Understand, generate, and analyze individual (I) and moving range (MR) control charts
  • Understand, generate, and analyze mean (Xbar) and range (R) control charts
  • Understand, generate, and analyze process capability analyses

Outline:

1. Primer on Statistical Analysis for Process Control and Capability
• Distribution
• Time series charts

2. Statistical Process Control and Capability
• Developing a control plan as part of a risk management strategy
• Statistical process control
• Process capability

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