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.

The course covers the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias.

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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.

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.

Instructors

dr-stephanie-kovalchik

Dr. Stephanie Kovalchik

Dr. Stephanie Kovalchik, is currently a Research Fellow within ISEAL (International Social and Environmental Accreditation and Labelling Alliance) and holds a joint appointment at Tennis Australia, where she works as a data scientist for the Game Intelligence Group.
See Instructor Bio

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

2020

Oct 16, 2020 to Nov 13, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

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

What Our Students Say​

I will be using the methodology of meta-analysis for my dissertation which is on effective schools. This will help me in my work as I am a principal of an elementary school and want to know which strategies have the greatest impact on student achievement

Debra Prenkert
Monroe County Community School Corporation

More and more medical students are submitting systematic reviews and associated meta-analyses, to meet research exposure requirements, in the Basic Sciences Programs of their medical school education, here in the Caribbean. The course provided me with a unique and much appreciated opportunity to learn much more about the technique, its underlying principles, and how to correctly apply the tool whenever evaluating a collection of primary studies. Time and energies committed to the course have been very well spent.

William Keller

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Meta Analysis 2

This course will teach you advanced issues in meta-analysis and the statistical analyses that are used to synthesize summary data from a series of studies.
Topic: Statistics, Biostatistics | Skill: Intermediate | Credit Options: CEU
Class Start Dates: Aug 21, 2020, Aug 20, 2021

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.
Topic: Statistics, Biostatistics | Skill: Intermediate | Credit Options: CEU
Class Start Dates: Oct 16, 2020

Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Meta Analysis in R
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