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.
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?
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.
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.
Learning objectives
The goal of this seminar is to teach you enough statistics to:
• Understand the statistical portions of most articles in medical journals.
• Do simple calculations, especially ones that help in interpreting published literature.
• Avoid being misled by foolish findings.
• Knowledge of which test when, why, and how.
• Perform simple analyses in statistical software.
• Communicate statistical findings to others more clearly.
Who will Benefit:
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?
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.
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.
Learning objectives
The goal of this seminar is to teach you enough statistics to:
• Understand the statistical portions of most articles in medical journals.
• Do simple calculations, especially ones that help in interpreting published literature.
• Avoid being misled by foolish findings.
• Knowledge of which test when, why, and how.
• Perform simple analyses in statistical software.
• Communicate statistical findings to others more clearly.
Who will Benefit:
- Physicians
- Clinical Research Associates
- Clinical Project Managers/Leaders
- Sponsors
- Regulatory Professionals who use statistical concepts/terminology in reporting
- Medical Writers who need to interpret statistical reports
- Clinical research organizations, hospitals, researchers in health and biotech fields.
- Persons working in the medical or health sciences, pharmaceutical and or nutriceutical industries, clinical trials, clinical research, and clinical research organizations, physicians, medical students, graduate students in the biological sciences, researchers, and medical writers who need to interpret statistical reports.
Day 1: Basics
12:00 -1:30 PM EST
Session 1: 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
Session 2: The many ways of interpretation
· Confidence intervals
· p-values
· Effect sizes
· Clinical vs. meaningful significance
Session 3: Types of Data and Descriptive Statistics
· Levels of data: Continuous, Ordinal, Nominal
· Normal distribution and it’s importance
· Graphical representations of data
· Data transformations, when and how
3:00 – 3:10 Break
3:10-4:45
Session 4: Common Statistical Tests
· Comparative tests
· Simple and Multiple regression analysis
· Non-parametric techniques
4 :45 – 5 :00 Q&A
Day 2: Further Understanding in Clinical Research
12:00 -1:30 PM EST
Session 1: Other Tests
· Non-Parametric tests
· Test for equivalency
· Test for non-inferiority
Session 2: Power and Sample Size
· Theory, steps, and formulas for determining sample sizes
· Demonstration of sample size calculations with GPower software
1:40-3:00
Session 3: How to Review a Journal Article
· General steps on article review
· Determining the quality of a journal or journal article
· Looking for limitations (all studies have them)
· Review of a selection of journal articles to for quality and interpretation
Session 4: Developing a Statistical Analyis Plan
· Using FDA (for the U.S. audience) or MHRA (for U.K. audience) 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
Day 3: Special Topics
12:00 -1:30
Session 1: Logistic Regression
· When and why?
· Interpretation of odd ratios
· Presentation of logistic regression analysis and interpretation
· Fun with contingency tables
Session 2: Survival Curves and Cox Regression
· History, theory, and nomenclature of survival analysis
· Kaplan-Meier Curves and Log Rank Tests
· Proportional Hazards
· Interpretation of hazard ratios
· Presentation of KM curves and Cox regression analysis and interpretation
Session 3: Bayesian Logics
· A different way of thinking
· Bayesian methods and statistical significance
· Bayesian applications to diagnostics testing
· Bayesian applications to genetics
Session 4: Systematic Reviews and Meta-Analysis
· Why perform a systematic reviews and/or meta-analysis?
· A bit of history and reasoning for systematic reviews and/or meta analysis
· Terminology
· Steps in performing a Systematic Review
· Steps in performing a Meta-Analysis
Faculty
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.
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.
