Statistics is a useful decision making tool in the clinical research arena. When working in a field where a p-value can determine the next steps on development of a drug or procedure, it is imperative that decision makers understand the theory and application of statistics.
Why you should attend
Many statistical softwares are now available to professionals. However, these softwares were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?
This seminar provides a non-mathematical introduction to biostatistics and is designed for non-statisticians. And it will benefit professionals who must understand and work with study design and interpretation of findings in a clinical or biotechnology setting.
The focus of the seminar is to give you the information and skills necessary to understand statistical concepts and findings as applies to clinical research, and to confidently convey the information to others.
Emphasis will be placed on the actual statistical (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.
Who Will Benefit
- Clinical Research Associates
- Clinical Project Managers/Leaders
- Regulatory Professionals who use statistical concepts/terminology in reporting
- Medical Writers who need to interpret statistical reports
Lecture 1 (45 Mins) - Why Statistics?
- 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
Lecture 2 (45 Mins) - The many ways of interpretation
- Confidence intervals
- effect sizes
- Clinical vs. meaningful significance
Lecture 3 (45 Mins) - Common Statistical Tests
- Comparative tests
- Regression analysis
- Non-parametric techniques
Lecture 4 (45 Mins) - Bayesian Logic
- A different way of thinking
- Bayesian methods and statistical significance
- Bayesian applications to diagnostics testing
- Bayesian applications to genetics
Lecture 5 (45 Mins) - Interpreting Statistics - Team Exercise
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
Lecture 6 (45 Mins) - Study power and sample size
- Review of p-value, significance level, effect size
- Formulas, software, and other resources for computing a sample size
Lecture 7 (45 Mins) - Developing a Statistical Analysis Plan
- Using FDA guidance as a foundation, learn the steps and criteria needed to develop a statistical analysis plan (SAP)
- An SAP template will be given to all attendees
Lecture 8 (45 Mins) - Specialized topics/Closing Comments/Q&A
- Comparing Survival Curves
- Pharmocokinetics/Pharmacodynamics (PK/PD)
- Taking a holistic view to study design and interpretation
- Question and Answer session
Elaine Eisenbeisz is a private practice consultant based in Southern California. She has over 20 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 completed her graduate certification in Applied Statistics with Texas A & M University. Gig ‘em Aggies! Currently, she is finishing her graduate work in Applied Statistics at Rochester Institute of Technology.
Elaine is a member of The American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Elaine is also a member in good standing with the Better Business Bureau.
Current areas of interest include Bayesian inference, simulation and bootstrapping techniques, and predictive modeling.
When she isn’t crunching numbers you can find Elaine digging in her garden, playing her violin, cooking, or playing board games with friends.