Meta Analysis In R

Meta Analysis Using R

taught by Stephanie Kovalchik

Aim of Course:

Meta analysis, the ‘analysis of analyses’, is the term used to describe the quantitative synthesis of scientific evidence. The aim of this course is to introduce students to the fundamentals of meta-analysis and provide an in-depth review of tools for conducting meta-analyses in the R language. 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.

The course assumes introductory knowledge of R. There will be a brief review of R programming in the first part of the course and links to other courses for those who need a more extensive refresher.

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.

Course Program:

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


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

Meta Analysis In R

Who Should Take This Course:
Researchers familiar with R who wish to combine the results of multiple studies.

You should be familiar with introductory statistics.  Try these self tests to check your knowledge.

Familiarity with the issues of Sample Size and Power Determination (another course) is also helpful.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses
Course Text:
All needed reading materials will be provided.

You must have a copy of R for the course. Click here for information on obtaining a free copy. 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.



October 16, 2020 to November 13, 2020

Meta Analysis In R


October 16, 2020 to November 13, 2020

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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