Course Description
Built for non-statisticians
- Designed specifically for professionals with little or no statistical background
- Helps you confidently work with statisticians and data teams
WHAT MAKES THIS TRAINING DIFFERENT
- Practical, Not Mathematical
- No heavy formulas
- No advanced math required
- Focus on real-world application
WHAT YOU WILL LEARN
By attending, you will be able to:
- Understand key statistical concepts used in clinical trials
- Interpret p-values, confidence intervals, and significance correctly
- Identify appropriate statistical tests for different scenarios
- Evaluate research findings and avoid misleading conclusions
- Understand sample size, bias, and study design fundamentals
- Communicate statistical results clearly within your organization
- The training focuses on concepts, application, and interpretation — not complex formulas
The objective of this seminar is to provide every trainee with the information and skills that are mandatory to comprehend numerical concepts and answers as smears to scientific study and to positively convey the information to others.
Statistics is a valuable tool that is good and useful for making decisions in the medical research arena. When employed in a field where a p-value can determine the next steps in the development of a drug or procedure, it is authoritative that choice makers comprehend the philosophy and request of statistics.
Quite a few numerical software is now available to professionals. However, this software was industrialized for geometers and can often be unnerving to non-statisticians. How do you know if you are persistent in the right key, let unaided execution be the best test?
And it will profit specialists who must comprehend and work with study design and clarification of findings in a scientific or biotechnology setting.
Stress will be placed on the real numerical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.
Training Agenda
Day 1: Foundations of Statistics (Build Your Core Understanding)
Session 1: Why Statistics Matters
- Do we really need statistical tests?
- Sample vs. Population — understanding the difference
- What statistics can and cannot do
- Descriptive statistics & variability explained simply
Session 2: Interpreting Results with Confidence
- Confidence intervals demystified
- Understanding p-values (without confusion)
- Effect sizes and why they matter
- Clinical vs. meaningful significance
Session 3: Types of Data & Descriptive Analysis
- Continuous, Ordinal, and Nominal data
- Normal distribution and why it’s critical
- Graphical data representation
- When and how to transform data
Session 4: Common Statistical Tests (Practical Overview)
- Comparative statistical tests
- Simple & multiple regression analysis
- Non-parametric techniques
🗣 Live Q&A Session
Day 2: Advanced & Applied Statistical Methods
Session 1: Logistic Regression Made Simple
- When and why to use logistic regression
- Interpreting odds ratios clearly
- Presenting and explaining results
- Working with contingency tables
Session 2: Survival Analysis & Cox Regression
- Key concepts and terminology
- Kaplan-Meier curves & Log-Rank tests
- Proportional hazards explained
- Interpreting hazard ratios
- Presenting survival analysis results
Session 3: Bayesian Thinking
- A new way to interpret data
- Bayesian vs traditional statistics
- Applications in diagnostic testing
- Use cases in genetics
Session 4: Systematic Reviews & Meta-Analysis
- Why they are critical in research
- Key terminology and concepts
- Step-by-step systematic review process
- Conducting a meta-analysis
Day 3: Clinical Research & Real-World Application
Session 1: Specialized Statistical Tests
- Non-parametric methods
- Equivalency testing
- Non-inferiority testing
Session 2: Power & Sample Size (Make Your Study Valid)
- Key theory and calculation steps
- Determining appropriate sample size
- Hands-on demo using G*Power software
Session 3: Reviewing Scientific Literature
- How to critically review journal articles
- Assessing quality and credibility
- Identifying study limitations
Session 4: Developing a Statistical Analysis Plan (SAP)
- Step-by-step SAP development
- Aligning with regulatory expectations from FDA and MHRA
- Key components of a robust SAP
- Ready-to-use SAP template provided
Who will benefit?
- Physicians
- Medical Writers who need to interpret statistical reports
- Clinical Project Managers/Leaders
- Clinical Research Associates Sponsors
- Regulatory Professionals who use statistical concepts/terminology in reporting
- Clinical research organizations, hospitals, and researchers in health and biotech fields.
- People engaged in the medical sciences, medicinal and or nutraceutical industries, scientific trials, scientific research, and clinical research administrations, physicians, medicinal students, graduate students in the biological sciences, researchers, and medical writers who need to interpret statistical reports.
Learning objectives
The aim of this seminar is to educate you on enough statistics to:
- Perform simple analyses in statistical software.
- Avoid being misinformed by unwise findings.
- Communicate statistical findings to others more clearly.
- Comprehend the numerical portions of the greatest articles in medical journals.
- Do simple calculations, particularly ones that aid in interpreting published literature.
-
Knowledge of which test when, why, and how.
This live training seminar includes the following for each registered attendee:
- A copy of the presentation slides by download
- A certificate of participation for attendee training records
- Q/A Session
How to Attend:
All WCS Seminar live training programs audio and visuals are delivered via Go to Webinar with a basic system requirement of a computer with internet access. You do not require a Go to Webinar account to join WCS Seminar’ live training courses, participants receive an email invitation that provides the access you need to join the meeting.
Elaine Eisenbeisz
Statistician ( 30 + yrs exp.)
Owner & Principal of Omega Statistics
Murrieta, California, United States
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine 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.
In addition to her technical expertise, Elaine possesses a talent for conveying statistical concepts and results in a way that people can intuitively understand.
Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M. She is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She has served as an investigator on many oncology trials. She also designs and analyzes studies as a contract statistician for pharmaceutical, nutriceutical and fitness companies and various clinical research organizations. Her work includes design and analysis for numerous private researchers and biotech start-ups as well as with larger companies such as Intutive, Allergan, and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.
