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Approaches[ edit ] In general, two types of evidence can be distinguished when performing a meta-analysis: The aggregate data can be direct or indirect. AD is more commonly available e. This can be directly synthesized across conceptually similar studies using several approaches see below.
On the other hand, indirect aggregate data measures the effect of two treatments that were each compared against a similar control group in a meta-analysis. For example, if treatment A and treatment B were directly compared vs placebo in separate meta-analyses, we can use these two pooled results to get an estimate of the effects of A vs B in an indirect comparison as effect A vs Placebo minus effect B vs Placebo.
IPD evidence represents raw data as collected by the study centers. This distinction has raised the need for different meta-analytic methods when evidence synthesis is desired, and has led to the development of one-stage and two-stage methods. Two-stage methods first compute summary statistics for AD from each study and then calculate overall statistics as a weighted average of the study statistics.
By reducing IPD to AD, two-stage methods can also be applied when IPD is available; this makes them an appealing choice when performing a meta-analysis.
Although it is conventionally believed that one-stage and two-stage methods yield similar results, recent studies have shown that they may occasionally lead to different conclusions.
Models incorporating study effects only[ edit ] Fixed effects model[ edit ] The fixed effect model provides a weighted average of a series of study estimates. Consequently, when studies within a meta-analysis are dominated by a very large study, the findings from smaller studies are practically ignored.
This assumption is typically unrealistic as research is often prone to several sources of heterogeneity; e. Random effects model[ edit ] A common model used to synthesize heterogeneous research is the random effects model of meta-analysis.
This is simply the weighted average of the effect sizes of a group of studies. The weight that is applied in this process of weighted averaging with a random effects meta-analysis is achieved in two steps: Inverse variance weighting Step 2: Un-weighting of this inverse variance weighting by applying a random effects variance component REVC that is simply derived from the extent of variability of the effect sizes of the underlying studies.
This means that the greater this variability in effect sizes otherwise known as heterogeneitythe greater the un-weighting and this can reach a point when the random effects meta-analysis result becomes simply the un-weighted average effect size across the studies.
At the other extreme, when all effect sizes are similar or variability does not exceed sampling errorno REVC is applied and the random effects meta-analysis defaults to simply a fixed effect meta-analysis only inverse variance weighting.
The extent of this reversal is solely dependent on two factors: Indeed, it has been demonstrated that redistribution of weights is simply in one direction from larger to smaller studies as heterogeneity increases until eventually all studies have equal weight and no more redistribution is possible.
One interpretational fix that has been suggested is to create a prediction interval around the random effects estimate to portray the range of possible effects in practice. These advanced methods have also been implemented in a free and easy to use Microsoft Excel add-on, MetaEasy.
Thus it appears that in small meta-analyses, an incorrect zero between study variance estimate is obtained, leading to a false homogeneity assumption.Apply and evaluate strategic management theory in an international, industry and organizational context.
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