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  Training Agenda Biostatistics for the Non-Statistician Training Course
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Learn clinical biostatistics course for the Non-Statistician

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?

This seminar on medical biostatistics online course provides a non-mathematical introduction to biostatistics and is designed for non-statisticians. 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.

Seminar Includes Certificate, PDF copy of the Handouts, Q/A Session, Live Instructor-led 3 Days Web Seminar & Statistical Analysis Plan Template provided by the faculty.

Agenda Day 1: Basics

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

▪         Self-assurance intervals

▪         P-values

▪         Effect sizes

▪         Clinical vs. meaningful significance

 

Break - 10 mins

Session 3: Types of Data and Descriptive Statistics

▪         Levels of data: Incessant, Ordinal, Trifling

▪         Normal delivery and its standing

▪         Pictorial representations of data

▪         Data alterations, when and how

 

Break 10 mins

 

Session 4: Common Statistical Tests        

▪         Relative tests

▪         Simple and Manifold reversion examination

▪         Non-parametric methods

 

Q&A

Agenda Day 2: Special Topics

Session 1: Logistic Reversion

▪         When and why?

▪         Clarification of odd ratios

▪         Performance of logistic reversion analysis and clarification

▪         Fun with eventuality 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

 

Break 10 mins

Session 3: Bayesian Logics

▪         A different way of thinking

▪         Bayesian methods and statistical significance

▪         Bayesian applications to diagnostics testing

. Bayesian applications to genetics

Break 10 mins

 

Session 4: Methodical Appraisals and Meta-Analysis         

▪         Why is doing a methodical review and/or meta-analysis important?

▪         A bit of history and reasoning for systematic reviews and/or meta-analysis

▪         Vocabulary

▪         Steps in performing a Systematic Review

▪         Steps in performing a Meta-Analysis

 

Agenda Day 3: Further Understanding in Clinical Research

Session 1: Other Tests

▪         Non-Parametric tests

▪         Test for equivalency

▪         Test for non-inferiority

 

Break 10 mins

 

Session 2: Power and Sample Size

▪         Concept, steps, and plans for decisive sample sizes

Display of sample size calculations with G-Power software

Session 3: How to Review a Journal Article

▪         Overall steps on object evaluation

▪         Defining the quality of a periodical or journal article

▪         Looking for limitations (all studies have them)

 

Break 10 mins

 

Session 4: Developing a Statistical Analysis Plan

▪         Using FDA (for the U.S. audience) or MHRA (for the 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

 

Who is supposed to get the 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.


Testimonials from Clients

"Elaine is absolutely the best, nicest, easiest to work with and the most fabulous statistician that I have worked with. I cannot say enough about her. I have been in academic medicine for 25 years now and founded a small biotech company. With Elaine's help on data, we were just purchased by Sanofi-Aventis, the 3rd largest pharma company! She helped our company so very much and no question was ever left unanswerable. You are making an excellent choice!"

- Resa Levetan, MD - Co-Founder, Cure DM

"Elaine is a wonderful statistics-oriented resource person who has both technical knowledge and business savvy. Having assisted my needs for a quality statistician, Elaine provided me with a well-planned and timely portfolio to aid in my research. She is very personable, and I highly recommend her impeccable data services."

Cirrelia Thaxton, Ph.D - Education

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.

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