This webinar presents an introduction to Meta-analysis

1. Introduction to meta-analysis
By
Moses Ocan (Ph.D.)
Senior Lecturer, Department of Pharmacology &
Therapeutics, MakCHS
The Africa Centre for Systematic Reviews and Knowledge Translation
Makerere University College of Health Sciences

2. Glossary
• Review: A critical appraisal of a book, play or other work
• Systematic: Done or acting according to a fixed plan or system (methodical)
• Research synthesis: review of primary research on a given topic with a
purpose of integrating the findings (creating generalizations, conflict
resolution)
• Meta-analysis: a set of statistical methods for combining outcomes (effect
sizes) across different studies addressing the same research question

3. Types of Reviews
• Literature or narrative
review
• Mapping review
• Scoping review
• Overview of reviews
• Rapid review
• Methodological review
• Qualitative systematic
review
• Quantitative systematic
review
• Mixed methods review
• Meta-analysis

4. Meta-analysis
• The statistical combination of results from two or
more separate studies
– Treatment effect (or effect size) should be consistent
from one study to the next
– It is used to identify a common effect size
– When the effect varies from one study to the next, MA
may be used to identify the reason for the variation
2023/9/19

5. 4. Perform meta-analysis
Moher D et al. Arch Pediatr Adolesc Med 1998;152:915-20

6. Why perform a meta-analysis
• Results of single studies typically vary from one
study to the next
– This affects decision making
• Narrative reviews had been used
– However, largely subjective (different experts can come
to different conclusions
• Meta-analysis, by contrast, applies objective
statistical methods and can be used with any
number of studies

7. Why perform a meta-analysis
• To increase power:
– The chance of detecting a real effect as statistically
significant if it exists
• To improve precision:
– Estimation of an intervention effect can be improved
when it is based on more information
• To answer questions not posed by the individual
studies
• To settle controversies arising from apparently
conflicting studies or to generate new hypotheses

8. What does a meta-analysis entail?
• Most essential element of an analysis is a
thoughtful approach, to both its narrative and
quantitative elements. This entails consideration of
the following questions:
– Which comparisons should be made?
– Which study results should be used in each comparison?
– What is the best summary of effect for each comparison?
– Are the results of studies similar within each
comparison?
– How reliable are those summaries?

9. When is meta-analysis most useful?
• There is a moderate to large amount of empirical
work available
• The results are variable across studies
• The expected magnitude of the effect is relatively
weak
• The sample sizes of individual studies are limited for
some reason

10. Meta-analysis vs null hypothesis
significance testing
• Most statistical approaches commonly used in
biological sciences are based on null hypothesis
significance testing (P values)
• In contrast, meta-analysis is focused on assessment
of the magnitude of an effect of interest (=effect
size), the precision of its estimate and causes of
variation in effect size among studies

11. What is an effect size?
• Effect size expresses the magnitude of an effect of
interest (e.g. strength of the relationship between two
variables)
• Effect sizes are the common currency which allows
combination of results from different studies into a
single, standardized and comparable metric of
outcome

12. Effect size
• Outcomes of two study groups treated differently is
known as the ‘effect’, the ‘treatment effect’ or the
‘intervention effect’ e.g Odds ratios,
• A general framework for synthesis may be provided
by considering four questions:
– What is the direction of effect?
– What is the size of effect?
– Is the effect consistent across studies?
– What is the strength of evidence for the effect?
• MA provides a statistical method for bullets 1-3

14. Choosing the effect size measures

15. Interpretation of findings

19. How confident are you of the evidence?

20. How confident are you of the evidence?

21. Learning is not attained by chance.
It must be sought for with ardor and attended to with diligence.
Abigail Adams