Meta Analysis

dinesh.s

What is Meta analysis?

Meta-analysis is a statistical technique for amalgamating, summarizing, and reviewing previous quantitative research. 

  Selected parts of the reported results of primary

studies are entered into a database, and this "meta-data" is "meta-analyzed", in similar ways to working with other data - descriptively and then inferentially to test certain hypotheses.

Meta analysis can be used as a guide to answer the question 'does what we are doing make a difference to X?', even if 'X' has been measured using different instruments across a range of different people. 

Meta-analysis provides a systematic overview of quantitative research which has examined a particular question.

Meta-analysis has been used to give helpful insight into: o the overall effectiveness of interventions (e.g.,

psychotherapy, outdoor education), othe relative impact of independent variables

(e.g., the effect of different types of therapy), and

othe strength of relationship between variables.

In statistics, a meta-analysis combines the results of several studies that address a set of related research hypotheses. This is normally done by identification of a common measure of effect size, which is modeled using a form of meta-regression.

Why do a meta-analysis?

 

we often have a lot of information, from many studies, sometimes contradictory, and meta-analysis offers us a tool to help us integrate this.

A meta-analysis may increase statistical power, resolve uncertainty, improve estimates of effect size, and may in fact be able to address questions not posed when the studies were designed.

to understand both the statistical, substantive, and methodological issues both in the original studies and in the meta-analysis.

How is a meta-analysis done?

1. Decide on the topic.

2. Decide on the hypothesis being tested.

3. Review the literature for all studies which test that hypothesis. (computerized search of the literature, careful study of the references in articles, examination of papers, abstracts, and presentations not published, and other sources of unpublished). This needs to be done carefully to minimize bias.

4. Evaluate each study carefully.

5. Create a database containing the information necessary for the analyses.

6. Perform the meta-analysis

7. Interpret the results.

When is a meta-analysis appropriate?

A meta-analysis is appropriate : When researcher have multiple studies which test the

same or similar hypotheses When researcher have numerous contradictory

studies when trying to review a complex literature However, the studies involved need to contain

sufficient information for the meta-analysis to be meaningful, and for the meta-analyst to evaluate the assumptions properly.

Imposes a discipline on the process of summing up research findings.

Represents findings in a more differentiated and sophisticated manner than conventional reviews.

Capable of finding relationships across studies that are obscured in other approaches.

Protects against over-interpreting differences across studies.

Can handle a large numbers of studies. (this would overwhelm traditional approaches to review)

Strengths of Meta-Analysis

Requires a good deal of effort Mechanical aspects don’t lend

themselves to capturing more qualitative distinctions between studies

“Apples and oranges”; comparability of studies is often in the “eye of the beholder”

Most meta-analyses include “blemished” studies

Weaknesses of Meta-Analysis

Sources of bias are not controlled by the method. Selection bias posses continual threat.

Analysis of between study differences is fundamentally correlational.

Heavy reliance on published studies, which may increase the effect as it is very hard to publish studies that show no significant results. This publication bias or "file-drawer effect" (where non-significant studies end up in the desk drawer instead of in the public domain) should be seriously considered when interpreting the outcomes of a meta-analysis.

Weaknesses of Meta-Analysis

Meta-analysis adds together apples and oranges. Overgeneralization can occur just as easily

Meta-analysis ignores qualitative differences between studies

Meta-analysis ignores study quality. Meta-analysis cannot draw valid conclusions

because only significant findings are published.

Meta-analysis is regarded as objective by its proponents but really is subjective. Meta-analysis relies on shared subjectivity rather than objectivity.

Criticisms of Meta-Analyses  

Meta analysis is a useful methodology for making sense out of conflicting research studies.

Make findings from individual studies more applicable to clinical practice.

Evidence-based practice to involve and integrate the best available evidence from research with clinical expertise and patient values to achieve optimal patient outcomes.

Conclusion

Than

k yo

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