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.

Familiarity with the issues of Sample Size and Power Determination (another course) is also helpful.
Organization of the Course:
Options for Credit and Recognition:
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 18, 2019 to November 15, 2019 October 16, 2020 to November 13, 2020

Meta Analysis In R


October 18, 2019 to November 15, 2019 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: Click here 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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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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