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Review
. 2021 Nov 5;22(6):bbab324.
doi: 10.1093/bib/bbab324.

Reviewing and assessing existing meta-analysis models and tools

Funmilayo L Makinde  1 , Milaine S S Tchamga  2 , James Jafali  3 , Segun Fatumo  4 , Emile R Chimusa  5 , Nicola Mulder  6 , Gaston K Mazandu  7
Affiliations
Review

Reviewing and assessing existing meta-analysis models and tools

Funmilayo L Makinde et al. Brief Bioinform. .

Abstract

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Keywords: cohort study; data integration; experimental study; meta-analysis; predictive power; sample size.

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Figures

Figure 1
Figure 1
Number of publications on the application of meta-analysis to GWAS or gene expression datasets over the years. Search query used in PubMed search: [(meta-analysis[Title/Abstract]) AND (gene expression[Title/Abstract]) OR (genome wide association[Title/Abstract])].
Figure 2
Figure 2
Timeline of existing meta-analysis methods showing the evolution of the meta-analysis model development. (Fisher [8], MinP [19] Stouffer [18], MaxP [21], FE [37], Good [13], weighted z-score [33], RE [10, 20], RankProd/Ranksum [15, 41], adaptive weighted Fisher [16], RE2 [22], BE [10] and roP [17]).
Figure 3
Figure 3
Decision tree of meta-analysis methods with respect to specific datasets. Method abbreviations: FE - Fixed effect, RE - Random effect, RE2 - Han and Eskin’s random effect, BE - Binary effect, rOP - rth ordered P-value, minP - Minimum P-value, maxP - Maximum P-value.
See this image and copyright information in PMC

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