Introduction to Meta-Analysis

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Organisation
UCL
Start - End
1 Jun - 2 May
Study Options
Remote
Fee
£ 150 - 225
Contact Name
Centre for Applied Statistics Courses
Contact Email
ich.statscou@ucl.ac.uk

01 June 2020–02 June 2020, 9:30 am–1:00 pm

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.

Day 1

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 day, 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.

Day 2 (optional)

On the 2nd, optional, half-day of the course, the theory of day 1 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 1 day 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'.


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