1. Narrative Reviews Limitations: Subjectivity inherent:
different criteria for choosing studies (larger studies, quality, comparable weight to all studies)
Lack of transparency (substantial body of evidence vs lower threshold)
narrative reviews come to opposite conclusions

2. Narrative Reviews Less useful as more information becomes available
Possibility of synthesizing data from a few studies
Treatment effect will vary as a function of study level covariates

3. Systematic Review A clear set of rules for searching studies
Clear inclusion and exclusion criteria
Transparency in the mechanisms of decision making
Most systematic reviews end up with a meta analysis

4. Meta analysis Despite narrative studies,
Weights assigned to each study are based on mathematical criteria determined advance
Based on a transparent, objective, and replicable framework
Meta analysis use extensions of formulas in primary studies

5. Parameter v.s. Statistics

7. Parameter v.s. Statistics

8. Parameter v.s. Statistics
RISK

9. Parameter v.s. Statistics
RATE

10. Descriptive / Analytical Study
Descriptive studies: Usually Mean , Proportion
Analytical studies:
Relationship (Association, Correlation) or Difference
Size of association (Effect Size)

11. Effect Size Depends of the type of outcome variable:
Quantitative outcome in two groups (usually, difference in means)
Cholesterol
Mean.s
Smoker
Mean.ns
Non-smoker
Mean.s – Mean.ns
Effect size

12. Effect Size
Quantitative outcome in a pre-post study (usually, mean of difference)
Cholesterol
Difference
Post
Pre
Mean.diff
Mean2
Mean1

13. Effect Size
Binary outcome in two groups ( risk ratio, odds ratio, risk difference)
Infant Mortality
Birthweight
Total
Low
Normal
Death
21,054
14,442
35,496
Live at 1 year
271,269
3,804,294
4,075,563
292,323
3,818,736
4,111,059
Risk
(21054/271269)=0.078
(14442/ )
= 0.004

14. Effect Size
Binary outcome in two groups ( risk ratio, odds ratio, risk difference)
Lung Cancer
Sum
Odds + --
Smoking
A (40)
B (20)
(A+B) 60
A/B=40/20
C (20)
D (30)
(C+D) 50
C/D=20/30
A+C (60)
B+D (50)
110

15. Effect Size
Quantitative variables (correlation)

19. Statistical Inference
Null hypothesis (no relationship, no difference)
Example: or
Example: or
Example:

20. Statistical Inference
In order to test Ho:
P_value (random relationship?)
(random effect size?)
Confidence Interval

21. Confidence Interval

22. Statistical Inference
95% CI for : (-0.25 , 0.78)
95% CI for OR: (1.2 , 4.8) P_value?
99% CI for RR: (0.8 , 1.7) P_value?
95% CI for correlation: (0.2, 0.9) P_value?

23. Precision
Confidence interval for the effect size reflects the precision of the estimation
Narrower confidence interval reflects more precision for the effect size
Some factors affecting precision:
Sample size
Study design (more precision in matched groups )

24. Meta Analysis
Choose an appropriate effect size and its confidence interval
The most used effect size in the selected articles
If some selected papers do not report the chosen effect size?
If different methods used for measuring a biochemical parameter?
If different questionnaires have been used for data collection

25. Heterogeneity
If results (effect sizes) are inconsistent, it may not be possible to estimate a summary effect size
Subgroup analysis
different inclusion & exclusion criteria (blood pressure and age)
different study designs (observational & interventional)

26. Heterogeneity Different methods for measuring the outcome
Row v.s. Adjusted Effect Size
Year of study
Another application of meta analysis
Finding the source of heterogeneity
Making new hypothesis

27. Meta Analysis Uses weights for studies for summarizing the effect size
What is the weight?
How calculate a weighted mean?

28. Meta analysis Fixed effect models (sampling error)
Random effect models (sampling & between studies dispersion)
Test of heterogeneity
Power of heterogeneity test
Summery confidence intervals in the two models

29. Impact of high dose v.s. std dose

31. Meta Regression Regression at subject level covariates
Meta regression at study level covariates