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Meta Analysis in R

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.


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

Dr. Stephanie Kovalchik

Dr. Stephanie Kovalchik

Dr. Stephanie Kovalchik is a data scientist at Zelus Analytics, a sports intelligence firm, and holds a joint appointment at Tennis Australia. Stephanie’s area of expertise is statistics. She received her PhD from UCLA, where she focused on multi-level modeling, prediction, and risk assessment. Stephanie has held appointments as a statistical researcher at the National Cancer Institute and the RAND Corporation, where she developed new statistical methods for handling complex health science data. While working in the health sciences, Stephanie was also conducting quantitative research in tennis.  She is the creator of the tennis analytics blog On The T and regularly writes on tennis on twitter @StatsOnTheT.

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


10/11/2024 to 11/08/2024
Instructors: Dr. Stephanie Kovalchik


10/10/2025 to 11/07/2025
Instructors: Dr. Stephanie Kovalchik


Familiarity with the issues of Sample Size and Power Determination is also helpful.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Meta Analysis in R

Additional Information


Homework in this course consists of data analysis exercises and programming in the R language.

Course Text

All needed reading materials will be provided.


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

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Meta Analysis in R