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.
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.
One of the reasons for the popularity of meta‐analysis is the notion that these analyses will possess more power to detect effects than individual studies. This is inevitably the case under a ...fixed‐effect model. However, the inclusion of the between‐study variance in the random‐effects model, and the need to estimate this parameter, can have unfortunate implications for this power. We develop methods for assessing the power of random‐effects meta‐analyses, and the average power of the individual studies that contribute to meta‐analyses, so that these powers can be compared. In addition to deriving new analytical results and methods, we apply our methods to 1991 meta‐analyses taken from the Cochrane Database of Systematic Reviews to retrospectively calculate their powers. We find that, in practice, 5 or more studies are needed to reasonably consistently achieve powers from random‐effects meta‐analyses that are greater than the studies that contribute to them. Not only is statistical inference under the random‐effects model challenging when there are very few studies but also less worthwhile in such cases. The assumption that meta‐analysis will result in an increase in power is challenged by our findings.
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