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Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies.
The aim of the PRISMA statement is to help authors improve the reporting of systematic reviews and meta-analyses. [3] PRISMA has mainly focused on systematic reviews and meta-analysis of randomized trials, but it can also be used as a basis for reporting reviews of other types of research (e.g., diagnostic studies, observational studies).
In an IPD meta-analysis, patient-level data from multiple studies or settings are combined to address a certain research question. IPD meta-analyses tend to be common for large-scale and international projects, and they are less limited than aggregate data (AD) meta-analyses in terms of the availability and quality of data they can use. [2]
Research synthesis or evidence synthesis is the process of combining the results of multiple primary research studies aimed at testing the same conceptual hypothesis. It may be applied to either quantitative [1] or qualitative research. [2] Its general goals are to make the findings from multiple different studies more generalizable and ...
Meta-analysis is a method used to combine the results of different trials in order to obtain a quantitative synthesis. The size of individual clinical trials is often too small to detect treatment effects reliably. Meta-analysis increases the power of statistical analyses by pooling the results of all available trials.
A meta-regression can be classified in the same way—meta-regression and network meta-regression—depending on the number of distinct treatments in the regression analysis. Meta-analysis (and meta-regression) is often placed at the top of the evidence hierarchy provided that the analysis consists of individual participant data of randomized ...
The same applies for the meta-analysed measure of effect: if the points of the diamond overlap the line of no effect the overall meta-analysed result cannot be said to differ from no effect at the given level of confidence. Forest plots date back to at least the 1970s. One plot is shown in a 1985 book about meta-analysis.
Funnel plots, introduced by Light and Pillemer in 1984 [1] and discussed in detail by Matthias Egger and colleagues, [2] [3] are useful adjuncts to meta-analyses. A funnel plot is a scatterplot of treatment effect against a measure of study precision.