Dublin, July 02, 2021 (GLOBE NEWSWIRE) -- The "Biostatistics for the Non-Statistician Training Course" conference has been added to ResearchAndMarkets.com's offering.
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 the 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.
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.
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 Should Attend:
Clinical Research Associates
Clinical Project Managers/Leaders
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
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
Clinical vs. meaningful significance
Session 3: Types of Data and Descriptive Statistics
Levels of data: Continuous, Ordinal, Nominal
Normal distribution and its importance
Graphical representations of data
Data transformations, when and how
Session 4: Common Statistical Tests
Simple and Multiple regression analysis
Day 2: Further Understanding in Clinical Research
Session 1: Other 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
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 for quality and interpretation
Session 4: Developing a Statistical Analysis 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
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
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 review and/or meta-analysis?
A bit of history and reasoning for systematic reviews and/or meta-analysis
Steps in performing a Systematic Review
Steps in performing a Meta-Analysis
For more information about this conference visit https://www.researchandmarkets.com/r/kv8ugh
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