Introduction to Meta-Analysis
23 November 2021–25 November 2021, 9:30 am–1:00 pm
This course provides an overview of meta-analysis from a statistician's point of view, with an optional half day workshop in R.
Centre for Applied Statistics Courses
Note: Due to the coronavirus outbreak, all courses will now be delivered online through a live video feed. You can expect the same level of group and individual support as you would have received in our face-to-face courses.
Meta-analysis is "the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings" (Glass, 1976)
We introduce the merits of meta-analysis and how it can form an important and informative part of a systematic review. We explain the most common statistical methods for conducting a meta-analysis and common issues that may be encountered along the way. At the end of the course, delegates should be able to conduct a meta-analysis of their own and interpret the results of meta-analyses published in journal articles.
The following topics are covered:
- An introduction to meta-analysis and its place in evidence-based research.
- Outcome measures and extracting relevant data from journal articles
- Fixed effect and random-effects models
- Heterogeneity between studies
- How to identify and deal with publication bias.
Related topics that we don't cover on this course are (1) how to conduct a systematic search of the literature, and (2) assessing the quality of studies in a meta-analysis.
A basic level of statistical literacy is required as a prerequisite. In particular, delegates should have a basic understanding of standard errors, p-values and confidence intervals. Those who have completed the five-day 'Introduction to Statistics and Research Methods' course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.
R supplement (optional)
In the optional half-day of the course, the theory is put in practice with the use of R (Rstudio) and real-world datasets. A basic knowledge of R programming is recommended as a prerequisite (taught on our course - 'Introduction to R'). If you are not sure whether you have sufficient knowledge in R, please take our short test in the separate tab titled 'Prerequisite test for R workshop'.
External Delegates (Non-UCL)
£150.00 (£225 inc. R supplement)
UCL Staff, Students, Alumni
£75.00* (£112.50 inc. R supplement)
Staff and Doctoral Students from ICH/GOSH
* Valid UCL email address and/or UCL alumni number required upon registration. Please note, this category does not include hospital staff unless you hold an official contract with the university.
† Limited free spaces available. If there are no free places remaining, Staff and Doctoral Students from ICH/GOSH can still register at the UCL rate.
Cancellations: Cancellation Policy
- Future Dates
Dates Time Apply
9.30am - 1.00pm^ Fully booked
^We recommend logging in 10 minutes prior to the scheduled start time to access materials and ensure the course can start promptly.
"I really enjoyed this course. I didn't know exactly what to expect but it will be very useful to me. I learnt a lot. Thanks!"
"This was an excellent course, thank you very much."
"Really relevant course, well put together and delivered."
"I found the interactive calculations reinforced the learning well."
"Good course, if there is more interesting courses that can help my PhD, I will definitely attend"
"This was a fantastic introductory course. The content was well thought out and explained very clearly. Also the best explanation of random effects models I've heard so far! Thanks for your hard work Dean"
"I found all the interactions with your institution, since of the beginning very oriented to my needs and giving support. Congratulations!"
- Prerequisite test for R workshop
To participate in the optional R workshop that takes place on the second day, we expect you to have enough experience in R to complete the following exercise.
First, click here to download the dataset we will use for this exercise and save it on your computer. The dataset is stored in an Excel file format (.csv).
Read in the dataset using the function called read.csv.
Using R, find how many rows and columns are in the dataset (using any code/method you prefer).
Calculate the mean of the variable called Start.IgM (hint: use the function called mean).
Create a new variable in the same dataset of the differences between Start.IgM and Stim.2.IgM.
Note: If you are not familiar with the functions read.csv and/or mean, you can view the help files by running the R code help(read.csv) or help(mean)