Meta Analysis is a systematic way to combine the findings (both Qualitative and Quantitative) from several selected studies to arrive at a single conclusion that has greater statistical power. Meta-Analysis is a type of systematic review (which answers the research questions by collecting and summarizing all empirical findings that are selected basis pre-specified criteria.
The conclusion based on meta-analysis is statistically stronger than that from each of the individual studies. This is primarily due to the increased sample size and greater diversity in the sample.
There are four primary applications where you can apply meta analysis:
1. When studies have conflicting results, meta analysis can be used to establish if the overall results from all the studies put together are statistically significant.
2. Meta-analysis combines results from multiple studies and this gives a more robust estimate about the magnitude of the effect under consideration.
3. Meta-analysis can be used to provide a more complete and complex analysis of harms, safety data, and benefits.
4. Lastly, meta-analysis can be used to examine subgroups with individual numbers that are not significant.
Some of the issues in adopting Meta-Analysis are:
1. The results of meta-analysis are derived by combining several similar studies, which is a difficult and time consuming task.
2. The findings from some of the selected studies may not be adequate for meta-analysis.
3. One of the issues that plague meta-analysis is that it combines findings from several studies and the study populations may be heterogeneous.
4. Lastly, advanced statistical techniques are required to combine results from multiple studies.
There are two important considerations to keep in mind while designing your methodology for a meta-analysis study:
1. Is the methodology of the selected studies similar (say RCT) or a mixture of different types?
2. Is there sufficient number of studies included and are there relevant studies with negative findings?