It is important for non-statisticians to become familiar with biostatistics

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Statistics is extremely useful as a decision-making tool in the clinical research arena. In areas such as working in a field where a p-value can determine the next steps on development of a drug or procedure, it is very handy, because of which it is imperative for decision makers to understand the theory and application of statistics.

Many statistical software applications have now been developed and made available to professionals. It needs to be borne in mind that these software applications were developed for statisticians, because of which its use can baffle non-statisticians. Their confusions could be as basic as pressing the right key, let alone performing the best test.

A full learning session on biostatistics for the non-statistician

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A seminar from GlobalCompliancePanel, a leading provider of professional trainings for all the areas of regulatory compliance, will throw light on the importance of biostatistics for the non-statistician.

Elaine Eisenbeisz, a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California, who has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations to start-up companies and individual researchers; will be the Director of this seminar.

Want to understand the importance of biostatistics for the non-statistician? Then, please enrol for this seminar by visiting It is important for non-statisticians to become familiar with biostatistics. This seminar has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

Statistical concepts in clinical research

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Designed essentially for non-statisticians; this seminar provides a non-mathematical introduction to biostatistics. It will be of high value to professionals who must understand and work with study design and interpretation of findings in a clinical or biotechnology setting.

Elaine will equip participants of this seminar with the information and skills necessary to understand statistical concepts and findings as they relate to clinical research. With this information, they will be able to confidently communicate with people with whom they need to.

Elaine will place emphasis on the actual statistical concepts, application, and interpretation. She will not go into the areas of mathematical formulas or actual data analysis. A basic understanding of statistics is desired from the participants, but is not necessary.

This course on biostatistics for the non-statistician will help professionals involved in this area, such as Physicians, Clinical Research Associates, and Clinical Project Managers/Leaders, Sponsors, Regulatory Professionals who use statistical concepts/terminology in reporting, and Medical Writers who need to interpret statistical reports.

Elaine’s agenda for this two-day seminar will consist of the following:

Why Statistics?

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  • Do we really need statistical tests?
  • Sample vs. Population
  • I’m a statistician not a magician! What statistics can and can’t do
  • Descriptive statistics and measures of variability

The many ways of interpretation

  • Confidence intervals
  • p-values
  • effect sizes
  • Clinical vs. meaningful significance

Common Statistical Tests

  • Comparative tests
  • Regression analysis
  • Non-parametric techniques

Bayesian Logic

  • A different way of thinking
  • Bayesian methods and statistical significance
  • Bayesian applications to diagnostics testing
  • Bayesian applications to genetics

Interpreting Statistics – Team Exercise

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  • Team Exercise: Review a scientific paper and learn how to
    • Interpret statistical jargon
    • Look for reproducibility, transparency, bias, and limitations
    • Convey information coherently to non-statisticians

Study power and sample size

  • Review of p-value, significance level, effect size
  • Formulas, software, and other resources for computing a sample size

Developing a Statistical Analysis Plan

Specialized topics/Closing Comments/Q&A

  • Comparing Survival Curves
  • Pharmacokinetics/Pharmacodynamics (PK/PD)
  • Taking a holistic view to study design and interpretation
  • Question and Answer session.

 

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Process Development and Validation rest on the right Design of Experiments and Statistical Process Control

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The application of DOE and SPC to the development, design and monitoring of manufacturing and testing requires the use of procedures. Why? It is because in a recent guidance document on Process Validation, the FDA has named the Quality Unit as being responsible in the review and interpretation of DOE and SPC studies.

The Quality Unit needs to take a practical orientation when it sets out doing this work. An approach in which case studies and examples are sprinkled goes a long way in helping Process Validation professionals in their work. This is exactly what a seminar from GlobalCompliancePanel, a leading provider of professional trainings for the regulatory compliance areas, will offer and help regulatory professionals in the Quality Control and Quality Assurance areas achieve their aims.

The Director of this two-day seminar is Dr. Steven Kuwahara, who Founder and Principal, GXP BioTechnology LLC. Want to understand ways by which to adapt the right approach to applying DoE and SPC to the development, design and monitoring of manufacturing and testing? Then, please register for this very useful session by logging on to Process Development and Validation rest on the right Design of Experiments and Statistical Process Control . This seminar has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

Completely interactive

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Dr. Kuwahara will do full justice to the role and importance of practical experiments in aiding an understanding of Design of Experiments. Theoretical information will be given only when it is needed to help gain an understanding of an experiment. At this highly interactive and practical session, Dr. Kuwahara will offer examples from real processes and testing procedures. He will intersperse these with examples that will be directly applicable to the areas of work that relate to the participants of this seminar.

An understanding of the way the process parameters relate to and work with each other is necessary for any pharmaceutical worker who is involved in performing, supervising or reviewing manufacturing or testing processes. The ability to monitor the performance of processes and test methods is also needed for such a worker. While this is true for a professional in any department of pharmaceuticals; it applies more to the worker who works in Quality Control and Quality Assurance, a requirement that has become necessary following the passage of recent FDA guidance document on Process Validation.

However, it is the development, manufacturing, or quality systems worker who carries out this work. In view of this fact, a high degree of coordination is needed between these two levels of employees. At this seminar, Dr. Kuwahara will arm these two levels of employees with the knowledge of the ways of designing the systems and studies, and then interpreting the results generated.

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Dr. Kuwahara has charted out the following agenda for this seminar:

Dietary Design of Experiments

  • Introduction
  • One Level, One Factor Designs. Simple Comparisons
  • Two-Level Multi-Factorial Design
  • Extracting Information from the Experiment

Statistical Process Control

Design of Experiments and Statistical Process Control

This seminar is designed for the benefit of professionals involved in the procedures and applications of DOE and SPC, such as Directors, Managers, Supervisors, Lead workers in Process Development, Manufacturing, Regulatory Affairs, Quality Assurance and Quality Control, and workers who participate in operations or the are involved in the supervision of the development, manufacturing, or testing of medicinal products.

To join us for more information, get in touch

The use of Applied Statistics for FDA Process Validation

Why you Should be Worried about HIPAAThe use of Applied Statistics for FDA Process Validation is considered a matter of very high importance in the pharmaceutical industry. The FDA’s guidance for the industry, which it called “Process Validation: General Principles and Practices”, was set up in 2011. This guideline sets the framework for Process Validation in the pharmaceutical industry. The FDA prescribes a three-stage process that any organization in the pharmaceutical industry has to set up:

  1. Process Design
  2. Process Qualification
  • Continued Process Verification.

The Process Design stage, which is called Stage 1, is when the organization defines the commercial manufacturing process. The knowledge that the organization has gained through development and scale-up activities serves as the basis for the development of this definition.

The Process Qualification, or Stage 2, involves evaluating the process design for the purpose of determining if the process defined in Stage I has the capability for reproducible commercial manufacturing.

The next stage of the FDA process validation stage is to determine if the Process Design stage and the Process Qualification stage give the ongoing assurance that the process remains in a state of control during routine production. This is what Stage 3, the Continued Process Verification, does.

Thorough understanding of how to implement Applied Statistics for FDA Process Validation

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The ways of using Applied Statistics for FDA Process Validation will be the topic of a two-day seminar that GlobalCompliancePanel, a leading provider of professional trainings for the regulatory compliance areas, will be organizing. At this seminar, Richard Burdick, Emeritus Professor of Statistics, Arizona State University (ASU) and former Quality Engineering Director for Amgen, Inc., will be the Director.

Please visit http://www.globalcompliancepanel.com/control/globalseminars/~product_id=901132SEMINAR?wordpress-SEO to register for this meaningful and highly valuable seminar on applied statistics for process validation. This course has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

A methodical approach to implementing statistical methodologies

Top 20 Costly Mistakes

The focus of this two-day course on Applied Statistics for FDA Process Validation is the various ways by which a systematic approach to implementing statistical methodologies into a process validation program consistent with the FDA guidance can be established.

Dr. Burdick will begin with a primer on statistics, where he will explain how the methods of Applied Statistics for FDA Process Validation seminar can be applied in each remaining chapter.

The two fundamental requirements for Process Validation, namely the application of statistics for setting specifications and assessing measurement systems (assays), will be taken up next.

The next aspect of applied statistics Dr. Burdick will move on to is how to apply statistics through the three stages of process validation as defined by requirements in the process validation regulatory guidance documents.

Since the methods taught through all these three stages are recommended by regulatory guidance documents; this seminar on Applied Statistics for FDA Process Validation will provide references to the specific citations in the guidance documents.

The aim of this learning on Applied Statistics for FDA Process Validation is to lead participants into ways of establishing a systematic approach to implementing statistical methodologies into a process development and validation program that is consistent with the FDA guidance.

Complete learning on Applied Statistics for FDA Process Validation

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Over the two days of this seminar, the participants will learn how to:

  • Apply statistics for setting specifications
  • Assess measurement systems (assays)
  • Use Design of Experiments (DOE)
  • Develop a control plan as part of a risk management strategy, and
  • Ensure process control/capability.

All concepts at this Applied Statistics for FDA Process Validation seminar are taught within the three-stage product cycle framework defined by requirements in the process validation regulatory guidance documents.

Although aimed at the pharmaceutical industry, this seminar on Applied Statistics for FDA Process Validation provides a useful framework for other related industries, as well.

In this important learning on Applied Statistics for FDA Process Validation; Dr. Burdick will cover the following areas:

  • Apply statistics to set specifications and validate measurement systems (assays)
  • Develop appropriate sample plans based on confidence and power
  • Implement suitable statistical methods into a process validation program for each of the three stages
  • Stage 1, Process Design: utilize risk management tools to identify and prioritize potential critical process parameters; and 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 and/or commercial) scale data; develop process performance qualification (PPQ) acceptance criteria by characterizing intra and inter-batch variability using process design data and batch homogeneity studies; and 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; and ensure your process is in (statistical) control and capable.

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Data visualization can have a great effect on statistical presentations

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It is said that a picture is worth a thousand words. This adage may appear figurative and allegorical when applied to the realm of statistics, but there is no denying the fact that a visual approach to statistics enlivens the subject like no other. A dash of pictorial work and some coloring at the right places enhance the presentation of otherwise drab statistical figures and slides dramatically.

Graphic displays illustrate the facts and truths behind statistics very artistically. They augment the appeal of what is conveyed by the statistical figures without altering or diluting the effect or the content. This is why presenting data visually is a great enhancer. It is very useful in any compliance analytics workflow.

Graphic enhancements can be used to uplift and raise the presentational aspects of statistics, but it requires skill and specific tools, because the pictorial aspect has to fit rightly into the statistical presentations. Any mismatch or mix-ups can have the opposite effect, making the statistical pictures gaudy, out of place and jarring.

Get to the ways of doing it

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A two-day seminar from GlobalCompliancePanel, a highly respected provider of professional trainings for the regulatory areas, will offer thorough and complete learning on the proper ways of beautifying statistical presentations with the right mix of graphics, so that the intended purpose of embellishing is met.

James Wisnowski, who is the cofounder of Adsurgo LLC and co-author of the book, Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP and currently provides training and consulting services to industry and government in Design of Experiments (DOE), Reliability Engineering, Data Visualization, Predictive Analytics, and Text Mining; will be the Director at this two-day seminar.

Please visit Data visualization can have a great effect on statistical presentations  to enroll for this highly interesting seminar, which has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

All about graphics in statistics

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This seminar on data visualization will present methods using which participants can interactively discover relationships graphically. James will offer participants the foundations that will help them create better graphical information with which to accelerate the insight discovery process and improve the comprehension of reported results.

He will lead participants to an exploration of the first principles and the “human as part of the system” aspects of information visualization from multiple leading sources such as Harvard Business Review, Edward Tufte, and Stephen Few. All this will be done using representative example data sets. Best practices for graphical excellence to most effectively, clearly, and efficiently communicate a thought will be explained. He will show how to construct visualizations for univariate, multivariate, time-dependent, and geographical data. Participants are encouraged to bring laptops to follow along demonstrations in JMP (free trial download at www.jmp.com), and open source solutions such as R (https://www.r-project.org) and Tableau Public (https://public.tableau.com/s/).

Meeting the requirements of analytic solutions

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As all regulated industries have now come to expect data-driven decisions; compliance regulations require analytic solutions. The starting point of these solutions is the development of data visualization for discovering relationships and finish with crisp graphs communicating results. To take the example of 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries; these documents specify the application of statistical methods for the following:

For each of these areas, data visualization serves as the foundation. Data visualization plays a major role in varying degrees in varied areas such as:

  • meeting FDA analytical requirements for third-tier critical to quality attributes for analytical bio similarity evaluations where graphical plots and tables alone may sufficiently address compliance criteria

 

  • HIPAA compliance, risk management and analysis, and many others of the quality functions.

James will show how to use data visualization and optimize the effect of these areas. The following agendas will be part of the two-day presentation:

  • Introduction and definitions
  • Examples of data visualizations for compliance and regulated industries
  • Historical context
  • Characteristics of data
  • Interactive data visualization exploration with Excel and websites
  • Human side of data visualization
  • Principles of good graphic design
  • Data visualization methodology
  • Best practices
  • Software introduction: JMP
  • Univariate plots
  • Distributions and histograms
  • Pie graphs, violin plots, pareto plots, box plots
  • Conditional formatting
  • Mulitvariate plots and heatmaps
  • Correlation
  • Multivariate scatterplots and density graphs
  • Contour plots
  • Categorical data plots: treemaps, mosaic plots
  • Software introduction: R
  • Software introduction: Tableau Public
  • Univariate plots with R and Tableau
  • Multivariate plots with R and Tableau
  • Dynamic and interactive graphs
  • Brushing, dynamic linking, and filtering
  • Profilers on response variables and optimization
  • Time series plots
  • Waterfall plots
  • Sparklines and trend lines
  • Statistical Process Control charts
  • Maps
  • Text data visualization
  • Dashboards
  • Course summary.