Meta‐analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on ...multiple related markers in each study (eg, various α‐diversity indices in microbiome studies). However, univariate meta‐analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta‐analyses are limited to the situations where marker‐by‐marker correlations are given in each study. Thus, here we introduce two meta‐analysis methods, multi‐marker meta‐analysis (mMeta) and adaptive multi‐marker meta‐analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker‐by‐marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P‐value among marker‐specific meta‐analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker‐specific pooled estimates while estimating marker‐by‐marker correlations non‐parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker‐specific meta‐analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α‐diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α‐diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at
https://github.com/hk1785/mMeta.
Early meta‐analyses in management research sought primarily to resolve seemingly conflicting findings by estimating a relationship’s population‐level effect size. Since then, management researchers ...have adopted increasingly sophisticated approaches that permit new theorizing, testing and comparing sophisticated models, and identifying boundary conditions. We summarize three of these approaches – i.e., qualitative meta‐analysis (QMA), meta‐analytic structural equation modeling (MASEM), and meta‐analytic regression analysis (MARA) – along with the special issue papers that adopt each approach. We conclude by raising three unresolved controversies that we believe deserve more attention and by offering our thoughts about how to maximize a meta‐analytic study’s chances for publication and impact.
Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ...ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts.
The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management.
Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentationBrings together experts from a broad range of fieldsShows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among speciesHelps you choose the right softwareDraws on numerous examples based on real biological datasets
Meta-analysis is the gold standard for synthesis in ecology and evolution. Together with estimating overall effect magnitudes, meta-analyses estimate differences between effect sizes via ...heterogeneity statistics. It is widely hypothesized that heterogeneity will be present in ecological/evolutionary meta-analyses due to the system-specific nature of biological phenomena. Despite driving recommended best practices, the generality of heterogeneity in ecological data has never been systematically reviewed. We reviewed 700 studies, finding 325 that used formal meta-analysis, of which total heterogeneity was reported in fewer than 40%. We used second-order meta-analysis to collate heterogeneity statistics from 86 studies. Our analysis revealed that the median and mean heterogeneity, expressed as I², are 84.67% and 91.69%, respectively. These estimates are well above "high" heterogeneity (i.e., 75%), based on widely adopted benchmarks. We encourage reporting heterogeneity in the forms of I² and the estimated variance components (e.g., τ²) as standard practice. These statistics provide vital insights in to the degree to which effect sizes vary, and provide the statistical support for the exploration of predictors of effect-size magnitude. Along with standard meta-regression techniques that fit moderator variables, multi-level models now allow partitioning of heterogeneity among correlated (e.g., phylogenetic) structures that exist within data.
Aims
To conduct a traditional meta‐analysis and a Bayesian Network meta‐analysis to synthesize the information coming from randomized controlled trials on different socket grafting materials and ...combine the resulting indirect evidence in order to make inferences on treatments that have not been compared directly.
Materials and Methods
RCTs were identified for inclusion in the systematic review and subsequent statistical analysis. Bone height and width remodelling were selected as the chosen summary measures for comparison. First, a series of pairwise meta‐analyses were performed and overall mean difference (MD) in mm with 95% CI was calculated between grafted versus non‐grafted sockets. Then, a Bayesian Network meta‐analysis was performed to draw indirect conclusions on which grafting materials can be considered most likely the best compared to the others.
Results
From the six included studies, seven comparisons were obtained. Traditional meta‐analysis showed statistically significant results in favour of grafting the socket compared to no‐graft both for height (MD 1.02, 95% CI 0.44–1.59, p value < 0.001) than for width (MD 1.52 95% CI 1.18–1.86, p value <0.000001) remodelling. Bayesian Network meta‐analysis allowed to obtain a rank of intervention efficacy.
Conclusions
On the basis of the results of the present analysis, socket grafting seems to be more favourable than unassisted socket healing. Moreover, Bayesian Network meta‐analysis indicates that freeze‐dried bone graft plus membrane is the most likely effective in the reduction of bone height remodelling. Autologous bone marrow resulted the most likely effective when width remodelling was considered. Studies with larger samples and less risk of bias should be conducted in the future in order to further strengthen the results of this analysis.
Context: Currently, most systematic reviews and meta-analyses are done retrospectively with fragmented published information. This article aims to explore the growth of published systematic reviews ...and meta-analyses and to estimate how often they are redundant, misleading, or serving conflicted interests. Methods: Data included information from PubMed surveys and from empirical evaluations of meta-analyses. Findings: Publication of systematic reviews and meta-analyses has increased rapidly. In the period January 1, 1986, to December 4, 2015, PubMed tags 266,782 items as "systematic reviews" and 58,611 as "meta-analyses." Annual publications between 1991 and 2014 increased 2,728% for systematic reviews and 2,635% for meta-analyses versus only 153% for all PubMed-indexed items. Currently, probably more systematic reviews of trials than new randomized trials are published annually. Most topics addressed by meta-analyses of randomized trials have overlapping, redundant meta-analyses; same-topic meta-analyses may exceed 20 sometimes. Some fields produce massive numbers of meta-analyses; for example, 185 meta-analyses of antidepressants for depression were published between 2007 and 2014. These meta-analyses are often produced either by industry employees or by authors with industry ties and results are aligned with sponsor interests. China has rapidly become the most prolific producer of English-language, PubMed-indexed meta-analyses. The most massive presence of Chinese meta-analyses is on genetic associations (63% of global production in 2014), where almost all results are misleading since they combine fragmented information from mostly abandoned era of candidate genes. Furthermore, many contracting companies working on evidence synthesis receive industry contracts to produce meta-analyses, many of which probably remain unpublished. Many other meta-analyses have serious flaws. Of the remaining, most have weak or insufficient evidence to inform decision making. Few systematic reviews and meta-analyses are both non-misleading and useful. Conclusions: The production of systematic reviews and meta-analyses has reached epidemic proportions. Possibly, the large majority of produced systematic reviews and meta-analyses are unnecessary, misleading, and/or conflicted.
Meta‐analysis has become the conventional approach to synthesizing the results of empirical economics research. To further improve the transparency and replicability of the reported results and to ...raise the quality of meta‐analyses, the Meta‐Analysis of Economics Research Network has updated the reporting guidelines that were published by this Journal in 2013. Future meta‐analyses in economics will be expected to follow these updated guidelines or give valid reasons why a meta‐analysis should deviate from them.
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