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.
The 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.
Learning objectives
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.
Who Should Attend:
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.
Key Topics Covered:
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
Key Topics Covered:
Elaine Eisenbeisz Owner & Principal Omega Statistics
Elaine Eisenbeisz is aprivate 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 solutionsfor industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
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, and is currently finishing her graduate studies at Rochester Institute of Technology.
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