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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).
Examples of such works are scientific reviews and meta-analyses. These and related practices face various challenges and are a subject of metascience. Various issues with included or available studies such as, for example, heterogeneity of methods used may lead to faulty conclusions of the meta-analysis. [115]
A meta-analysis with some or all studies having more than two arms is called network meta-analysis, indirect meta-analysis, or a multiple treatment comparison. Despite also being an umbrella term, meta-analysis sometimes implies that all included studies have strictly two arms each—same two treatments in all trials—to distinguish itself ...
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]
Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET. It may also refer to a specific piece of software created by the SDM Project to carry out ...
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
CMA does not solve meta-analysis's problem of "garbage in, garbage out". However, when a class of studies is deemed garbage by a critic, it does offer a way of examining the extent to which those studies may have changed a result. Similarly, it offers no direct solution to the problem of which method to choose for combination or weighting.
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