The document discusses the methodology and advantages of meta-analysis and systematic reviews, emphasizing the importance of evidence strength and types. It details various phases of conducting meta-analyses, including data collection, pooling results using different models, and addressing limitations such as potential biases. Key references and statistical techniques used in these analyses are also highlighted.

1. META-ANALYSIS/
SYSTEMATIC REVIEW

2. Updating
New Techniques
33100 active English language, peer reviewed
journals
Over 3 million articles a year
Medical Information

3. Narrative Review
Narrative
Voting
Systematic Reviews

4. The Five Strengths of Evidence
Type Strength of evidence
I Strong evidence from at least one systematic
review/meta-analysis of multiple well-designed
randomized controlled trials
II Strong evidence from at least one properly
designed randomized controlled trial of
appropriate size
III Evidence from well designed trials without
randomization e.g. pre-post, cohort, matched
case-control studies.
IV Evidence from well designed non-experimental
studies from more than 1 centre
V Opinions of respected authorities, descriptive
studies or reports of experts

5. History
Pearson (1904) Typhoid Fever Prevention: Serum Inoculation
Goldberger (1907) Urinary Tract Infection in Typhoid Fever
Glass (1976) Meta-analysis
The National Library of Medicine (1989) Meta-analysis
Archie Cochrane (Died 1988)
Cochrane Collaboration (1992)

6. Definition
Webster Ninth New Collegiate Dictionary (1990)
Meta

7. 1. Simple
2. Systematic
3. Statistical Power
4. Best form of evidence
Advantages

8. 1. Location and selection of studies.
2. Studies with different designs, populations
and effects.
Heterogeneity
3. File Drawer Problem(loss of information
on important outcomes)
Limitations

9. 1. Research question formulation
2. Hypothesis
3. Localization of the Studies(identify
relevant studies)
- MEDLINE/ PubMed (from 1966 –
- PsycLIT (from 1887 -
- Cochrane Library
- Index Medicus
- Manual Review
Phases

10. 3. Selection of the localized studies
- Define Study Design
- Language
- Year of Publication
- Exposure or Intervention
- Effect or Outcome
Phases

11. 4. Data collection &
extraction
1.Quantitative Meta-analysis
-Pooling the Results
-Fixed Effect Model vs Random Effect Model
2.Qualitative Meta-analysis
Quality of Pooled Studies
e.g. Matched case control studies.

12. . Quantitative Meta-analysis
- Combing the Results
 = [ pi i ] / pi
pi Weight of every Study
i Parameter selected for investigation
Phases

13. . Quantitative Meta-analysis
- Combing the Results
Fixed Effect Model: differences amonf studies
are purely due to chance
Random effect: differences among studies due to
chance and other reasons also
1. Mantel Hanzel
2. Inverse of Variance
3. Attributable Risk
Pooling

14. Which model to be used
-When heterogeneity is absent……use
fixed effect
- When heterogeneity is present use
random model
- Some researcher suggesting use of both
model irrespective of heterogeneity

16. 1. Mantel Hanzel
Pooling
E x p o s e d D i s e a s e
P r e s e n t
D i s e a s e
A b s e n t
T o t a l
P r e s e n t A B A + B
A b s e n t C D C + D
T o t a l A + C B + D N
RRMH =  [ (ci bi / ni ) (ai di / bi ci ) ] /  (ci bi / ni )
Var RR = RRi [ 1/a + 1/b + 1/c + 1/d ]
RRMH 95% CI = RRMH x EXP [ + 1,96  Var RR (In RRMH)]

17. Pooling
2. Inverse of Variance
RRW = Exp [ wi In RRi ] / wi
RRW Global Relative Risk.
wi Weight of every Study: Inverse of Variance of RR.
RRi Relative Risk of every Study
RR refers to RR, SMR, OR or POR
RRW 95% CI = In RRW * Exp [ + 1,96  Var (In RRw)]

18. 3. Attributalbe Risk.
RDpooled =  wi RDi / wi
Weight: Inverse of Variance of every Study.
wi
RDi Risk Differences (Attributable Risk) of every Study.
Pooling

22. Test of Homogeneity
Cochrane’s Q: a statistic based on the chi-squared test
X2 =  ( In RRi - RRW ) 2 / V ( In RRi )
Significant Heterogeneity: < 0,05 or < 0,1
The test of homogeneity has low power
Random Effect Model
Pooling

23. Random Effect Model
RRA = Exp [ w*i In RRi ] / w*i
W*I will be calculated as follow:
W *i = 1/ [ V (In ORi) + VAR]
VAR = [Chi Square – (N-1)] / U
U = (N – 1) [WM - { var (w) / N* WM}]
Results of both Fixed Effect Model and
Random Effect Model
Pooling

24. Is the Weight of the Study ONLY Enough
for Pooling?
Quality Score for every Study
RRpooled = Σ (qw) RRi / Σ (qw)
q Quality Value of original Study
(between 0.0 to 1.0).
Pooling

29. How Many Studies Could Change The Results
ToAccept The Null Hypothesis?
Tolerance Index
N = [ K {K(RRW) 2 – 2,706} ] / 2,706
N Number of Studies not considerded and
with Null Results
K Number of Studies Included in the Review
Publication Bias

30. Phases
Combining the Results
- Heterogeneity
Stratified Analysis
Metaregression
Sensitivity analysis
6. Results

31. Phases
7. Conclusions
Limitations
e.g. Before reaching conclusions based on the present
results, it is necessary to consider several potential
objections to our procedures. Methodological concerns
include limitations in the quality of the primary data, as
the usefulness of meta-analysis is largely dependent on
the quality of the studies used. Combining randomized
controlled trials provides more evidence, but combining
data from observational studies is sometimes desirable,
especially in studying the etiology of chronic diseases.

33. References
 Higgins JPT, Thomas J, Chandler J, Cumpston M, Li
T, Page MJ, Welch VA(editors). Cochrane Handbook
for Systematic Reviews of Interventions version
6.3 (updated February 2022). Cochrane, 2022.
Available from: www.training.cochrane.org/handbook.
 Hodgson R. What is meta-analysis? Hayward Medical
Communications 2014. Available from:
https://www.whatisseries.co.uk/wp-
content/uploads/woocommerce_uploads/2020/04/What-
is-meta-analysis.pdf
 Fleiss JL. The statistical basis of meta-analysis.
Stat Methods Med Res 1993;2(2):121-45.

34. Statistical Packages
 Review Manager (RevMan) Version 5.
 Comprehensive Meta-analysis (CMA).
 MetaXL.
 STATA.