This document explores meta-analysis techniques in epidemiology, outlining its advantages, disadvantages, and historical background. It emphasizes the importance of combining data from multiple studies to overcome limitations of individual studies and to increase the reliability and precision of findings. The document also highlights various models used in meta-analysis, the assessment of publication bias, and its application in animal and veterinary science.

1. Meta-analysis Techniques in
Epidemiology
Collate, analyze and conclude using results of several
related studies.
Dr Sakshi Dubey, M.V.Sc. Scholar, Division of Epidemiology
&
Dr. BR Singh, Head Division of Epidemiology Indian Veterinary Research
Institute, Izatnagar & Director CCS NIAH, Baghpat

2.  Analysis of analyses
 Collate, analyze and conclude using results of several related studies
 The statistical analysis of a collection of analytic results for the purpose of
integrating their findings (DerSimonian and Laird,
1986)
 Published studies from literature are combined (Berman and Parker,
2002)
 Weighted analysis of summary statistics (Bravata and Olkin,
2001)
 Frequently used for clinical trials
 Benefits of Meta-analysis
 Offers more reliable information
 Increases precision in estimating effects
Meta-analysis

3. Meta-analysis ???
 Single study
 Deliver reliable information for only specific place or period
 Diverse results due to varying places or periods or both
 Incapable in providing final conclusions
 Interest
 How far results of individual studies are stable under varying
situations
 Provide valid results for wider population
 Combined statistical analysis is necessary
 To produce overall summary of result
 To determine consistency among different studies
 Meta-analysis
Overcomes the limits of size or scope in single studies.

4. History
 The 12th century in China, Chu Hsi (1130~1200)
formulated 'Theory of Systematic Rule’
 In Western World (17th century) studies of astronomy
 In 1904 Karl Pearson in the British Medical Journal,
published a paper on multiple clinical studies
 In the 1970s, meta analysis was introduced in
educational research, starting with the work of Gene V.
Glass, Frank L. Schmidt and John E. Hunter.

5. Function of Meta-Analysis
 Identifies heterogeneity
 Increases statistical power and precision of the study
 Develop ,refine, and tests hypothesis
 Calculates sample size for future studies
 Identifies data gaps
 Reduces the subjectivity of study comparisons

6. Advantages
 Focuses attention on trials as an evaluation tool to
increase the impact of trials on clinical practice.
 Encourages designing of good trial and increases
strength of conclusions.
 Make the results fit for generalising to a larger
population.
 Improves precision and accuracy of estimates
through use of more data sets.
 May increase the statistical power to detect an
effect.

7. Advantages
 Inconsistency of results across studies can be
quantified, analyzed and corrected.
 Hypothesis testing can be applied on summary
estimates.
 Moderators can be included to explain variation
between studies.
 The presence of publication bias can be
investigated.

8. Disadvantages
 Meta-analysis may discourage large definitive trials.
 Increases tendency to unwittingly mix different trials
and ignore differences.
 Potential for tension between meta-analyst and
conductors of original trials may introduce biasness.
 Meta-analysis of several small studies may not
predict the results of a single large study.
 Sources of bias are not controlled by the method
 A good meta-analysis of badly designed studies will
still result in bad statistics.

9. Steps of Meta-analysis
 Define the Research Question
 Perform the literature search
 Select the studies
 Extract the data
 Analyze the data
 Report the results

10. Study Sources
 Published literature
 citation indexes
 abstract databases
 reference lists
 contact with authors
 Unpublished literature
 Uncompleted research reports
 Work in progress

11. Quality Assessment
Study components
 Study design
 Outcome measurement
 Exposure measurement
 Response rate/follow-up rate
Analytic strategy
 Adjustment for confounding
Quality of reporting

12. Data Extraction
 Publication year
 Performing year
 Study design
 Characteristics of study population (n, age,
sex)
 Geographical setting
 Assessment procedures
 Risk estimate and variance
 Covariates

13. Funnel Plot
 “A funnel plot is used as a way to assess
publication bias in meta-analysis.”

14. Comparability of sources
 Key feature of component trial is the variability
(heterogeneity)
 Heterogeneity is variation between the studies’
results

15. Statistical measures of heterogeneity
 The Chi2 test measures the amount of variation in a
set of trials, and tells us if it is more than would be
expected by chance.
 I squared quantifies heterogeneity.
where Q = heterogeneity c2
statistic
Higgins and Thompson
(2002)
Q
dfQ
I

1002

16. Types of models are used to
produce summary effect measures
1
• Fixed Effect Model
2
• Random Effects Model
3
• Meta-Regression

17. Fixed effect model
 Inference is based on the studies actually done.
 The variance component of the summary effect is
only composed of terms for the within study variance
of each study.
 Confidence intervals too narrow.

18. Random Effect Model
 Inference is based on the assumption that studies
used in the analysis are a random sample of a
hypothetical population of studies.
 Variance component includes a between study
component as well as a within study component.
 Confidence interval is wide or wider than in fixed
effect model.

19. Models and Measures
Model Effect Assumption Methods Measures
Fixed effect model Mantel – Haenszel
approach
Ratios (Odds -ratios,
rate ratios, risk ratio)
Peto method Odds ratio
General Variance
Based
Ratio all types and
rate difference
Random effect model DerSimonian and
Laird
Ratio (all types) and
rate difference

20. ^
 Meta-regression is a tool used in meta-analysis to
examine the impact of moderator variables on study
effect size using regression-based techniques.
 Meta-regression is a technique which allows
researchers to explore the types of patient-specific
factors or study design factors contributing to the
heterogeneity.
Meta-Regression

21. Forest plot
The graphical display of results from individual
studies on a common scale is a “Forest plot”

22.  Useful tool for epidemiological studies which investigates the
relationships between certain risk factors and disease. (Dutton, 2010)
 Useful tool to improve animal well-being and productivity
 Despite of a wealth of suitable studies it is relatively underutilized in
animal and veterinary science. Lean et al. (2009)
 Meta-analysis can provide reliable results about diseases occurrence,
pattern and impact in livestock.
 It is utmost essential to take benefit of this statistical tool for produce.
more reliable estimates of concern effects in animal and veterinary
science data.
Meta-analysis for animal and
veterinary science

23. Conclusion
 Prior to conducting a meta-analysis, it is necessary to
determine if the purpose is to explore sources of
heterogeneity or to calculate a summary effect size.
 Each Steps of Meta-analysis is very important.
 Source of data should be free from publication biasness.
 Follows GIGO principle of ‘garbage in, garbage out’.
 Like large epidemiologic studies, meta-analysis run the risk of
appearing to give results more precise and conclusive that are
warranted.