Elaine Eisenbeisz

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.

Overview:

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?

DAY ONE

Lecture 1: Why Statistics?

• Do we really need statistical tests?/li>
• 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: The many ways of interpretation

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

Lecture 3: Common Statistical Tests

• Comparative tests
• Regression analysis
• Non-parametric techniques

Lecture 4: Bayesian Logic

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

DAY TWO

Lecture 1: 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 2: Study power and sample size

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

Lecture 3: 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 4: 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(PK/PD)