Meta Analysis in R
The course covers the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias.
Overview
In this course, students are introduced to the fundamentals of meta-analysis and provide an in-depth review of tools for conducting meta-analyses in the R language. Meta analysis, the ‘analysis of analyses’, is the term used to describe the quantitative synthesis of scientific evidence.
The course will cover the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias.
Advanced topics will include the handling of rare events, missing data, and indirect treatment comparisons, among other topics.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After completion of this course, students will know how to apply standard methods of meta-analysis in R and will also have gained more experience with advanced R programming topics, such as function writing and reproducible reporting.
- Prepare data for analysis in R
- Define the outcome and effect type
- Distinguish and handle fixed and random effects models
- Visualize and interpret results
- Conduct meta regression
- Deal with missing data and rare events
Who Should Take This Course
Researchers familiar with R who wish to combine the results of multiple studies.
Our Instructors
Matt Bezdek
Matt Bezdek is a Senior Data Scientist at Elder Research. He has over 10 years of experience in performing advanced statistical analyses. At Elder Research he helps commercial and nonprofit clients with model validation, software development, interactive data visualization, data literacy education, and building data analytic strategies.
He holds a PhD in Cognitive Psychology from Stony Brook University and has conducted research at the Georgia Institute of Technology and Washington University in St. Louis.
Matt will teach our Regression, Meta Analysis in R, and Python for Analytics courses.
Course Syllabus
Week 1
Introduction to Meta Analysis
- History of Meta-Analysis
- Basics of Systematic Review and Meta-Analysis
- Review of the R language
- Meta-Analysis packages in R
- Reference Management
- Data Preparation for Meta-Analysis
Week 2
Types and Models for Effect Sizes
- Outcomes in Meta-Analysis
- Types of Effect
- Fixed Effects Model
- Random Effects Model
- Reporting, Forest Plots, and Interpretation
Week 3
Bias, Heterogeneity, and Meta-Regression
- Bias
- Evaluating and Reporting Bias
- Heterogeneity
- Assessing and Reporting Heterogeneity
- Meta-regression
Week 4
Advanced Topics
- Missing Data
- Individual Patient Data Meta-Analysis
- Rare Events and Small Studies
- Network Meta-Analysis
Class Dates
2025
Instructors: Matt Bezdek
Prerequisites
Familiarity with the issues of Sample Size and Power Determination is also helpful.
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Additional Information
Homework
Homework in this course consists of data analysis exercises and programming in the R language.
Course Text
All needed reading materials will be provided.
Software
You must have a copy of R for the course. You should also download RStudio (download here), an editing and development environment that is especially designed as a place to write R code. Both programs are free. After installing R in your computer you may also install several R add-on packages. Instructions for this installation will be provided as needed.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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