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.
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?
Why you should attend:
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.
- Clinical Research Associates
- Clinical Project Managers/Leaders
- Regulatory Professionals who use statistical concepts/terminology in reporting
- Medical Writers who need to interpret statistical reports
Day 1 Schedule
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
Clinical vs. meaningful significance
Common Statistical Tests
A different way of thinking
Bayesian methods and statistical significance
Bayesian applications to diagnostics testing
Bayesian applications to genetics
Day 2 Schedule
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
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
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.
Specialized topics/Closing Comments/Q&A
Comparing Survival Curves
Taking a holistic view to study design and interpretation
Question and Answer session
• Clinical Research Associates
• Clinical Project Managers/Leaders
• Regulatory Professionals who use statistical concepts/terminology in reporting
• Medical Writers who need to interpret statistical reports