Introduction to Meta-Analysis

Book Now (Online Course) 

Note: This course will be delivered exclusively online via zoom. You can expect the same level of group and individual support as you would have received in our face-to-face/hybrid courses.

Additionally, we now offer this as a self-paced, online course that you can register for and start at any time. Click below under the 'Online/Self-Paced Materials' tab to register and gain access:

Details

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 and Stata supplement (optional)

In the optional half-day of the course (only available in the live version of this 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' and 'Introduction to Stata'). 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'.

Fees

Course Fees (live)

Below are the course fees for all courses delivered live either face-to-face, or through online video feed.

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	FREE †

* 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

Course Fees (self-paced)

Below are the fees for access to the online, self-paced version of this course.

External Delegates (Non-UCL)	£75.00 (inc. VAT)
UCL Staff, Students, Alumni *	£37.50 (inc. VAT)
Staff from ICH/GOSH and doctoral students from SLMS *	FREE †

* A valid UCL email address and/or UCL alumni number required. Please note, this category does not include hospital staff unless you have a formal affiliation with the university. To get this discount, please email ich.statscou@ucl.ac.uk to confirm your eligibility and receive a code that can be entered at the checkout.

† If you are a doctoral student from SLMS (School of Life and Medical Sciences), you can get access to this, and all other self-paced courses developed by CASC by filling in this online form.

Future Dates

Dates	Time	Apply
Main Course 20th-21st May 2024	13.30 - 17.00^	Book Now
R Supplement 22nd May 2024	13.30 - 17.00^	Book Now
Stata Supplement 23rd of May 2024	13.30 - 17.00^	Book Now

^ For those attending online, we recommend logging in 10 minutes prior to the scheduled start time to access materials and ensure the course can start promptly at 1.30pm.

Online/self-paced materials

An online, self-paced version of this course is available that includes the following materials:

• Full electronic notes
• Short lecture videos (recorded outside of the classroom) that follow closely with the notes
• Interactive multiple choice quizzes
• Extended practical exercises (with solutions) for further comprehension

Support: The course includes unlimited support through a forum that will be manned by one of our teaching fellows. We aim to provide responses to all questions on Tuesday and Friday each working week. Note that questions can only relate directly to the course materials and should not be used as a consultancy service for your own projects.

Personalised certificates will be generated on completion.

UCL extend: The link below leads to the UCL extend store where this self-paced course is available for purchase. If you are eligible for a discount, then please email ich.statscou@ucl.ac.uk to receive a voucher code that can be used on checkout. UCL delegates should use their university email address to register, and external delegates can use any other account. 

Click to register (online, self-paced)

Alternatively, if you are a doctoral student from SLMS (School of Life and Medical Sciences), you can get access to this, and all other self-paced courses developed by CASC by filling in this online form.

Prerequisite test for R workshop

To participate in the optional R workshop, 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)

Feedback

"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!"