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The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses
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working on evidence synthesis receive industry contracts to produce meta-analyses, many of which probably remain unpublished.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.
Journal Article
A Guide to Conducting a Meta-Analysis with Non-Independent Effect Sizes
Conventional meta-analytic procedures assume that effect sizes are independent. When effect sizes are not independent, conclusions based on these conventional procedures can be misleading or even wrong. Traditional approaches, such as averaging the effect sizes and selecting one effect size per study, are usually used to avoid the dependence of the effect sizes. These ad-hoc approaches, however, may lead to missed opportunities to utilize all available data to address the relevant research questions. Both multivariate meta-analysis and three-level meta-analysis have been proposed to handle non-independent effect sizes. This paper gives a brief introduction to these new techniques for applied researchers. The first objective is to highlight the benefits of using these methods to address non-independent effect sizes. The second objective is to illustrate how to apply these techniques with real data in R and Mplus. Researchers may modify the sample R and Mplus code to fit their data.
Journal Article
Heterogeneity in ecological and evolutionary meta-analyses: its magnitude and implications
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.
Journal Article
What we learn about bipolar disorder from large‐scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group
MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness. This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.
Journal Article
Methodological Guidance Paper: High-Quality Meta-Analysis in a Systematic Review
This methodological guidance article discusses the elements of a high-quality meta-analysis that is conducted within the context of a systematic review. Meta-analysis, a set of statistical techniques for synthesizing the results of multiple studies, is used when the guiding research question focuses on a quantitative summary of study results. In this guidance article, we discuss the systematic review methods that support high-quality meta-analyses and outline best practice meta-analysis methods for describing the distribution of effect sizes in a set of eligible studies. We also provide suggestions for transparently reporting the methods and results of meta-analyses to influence practice and policy. Given the increasing use of meta-analysis for important policy decisions, the methods and results of meta-analysis should be both transparent and reproducible.
Journal Article
Quantifying Publication Bias in Meta-Analysis
Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect size estimate and are usually employed as sensitivity analyses to assess the potential impact of publication bias. Punnel-plot-based methods include visual examination of a funnel plot, regression and rank tests, and the nonparametric trim and fill method. Although these approaches have been widely used in applications, measures for quantifying publication bias are seldom studied in the literature. Such measures can be used as a characteristic of a meta-analysis; also, they permit comparisons of publication biases between different meta-analyses. Egger's regression intercept may be considered as a candidate measure, but it lacks an intuitive interpretation. This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias. This measure describes the asymmetry of the collected studies' distribution. In addition, a new test for publication bias is derived based on the skewness. Large sample properties of the new measure are studied, and its performance is illustrated using simulations and three case studies.
Journal Article
Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis
Background
Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools.
Implementation
Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process.
Results
Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users.
Conclusions
Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.
Journal Article
Methodological issues and advances in biological meta-analysis
Meta-analysis has changed the way researchers conduct literature reviews not only in medical and social sciences but also in biological sciences. Meta-analysis in biological sciences, especially in ecology and evolution (which we refer to as ‘biological’ meta-analysis) faces somewhat different methodological problems from its counterparts in medical and social sciences, where meta-analytic techniques were originally developed. The main reason for such differences is that biological meta-analysis often integrates complex data composed of multiple strata with, for example, different measurements and a variety of species. Here, we review methodological issues and advancements in biological meta-analysis, focusing on three topics: (1) non-independence arising from multiple effect sizes obtained in single studies and from phylogenetic relatedness, (2) detecting and accounting for heterogeneity, and (3) identifying publication bias and measuring its impact. We show how the marriage between mixed-effects (hierarchical/multilevel) models and phylogenetic comparative methods has resolved most of the issues under discussion. Furthermore, we introduce the concept of across-study and within-study meta-analysis, and propose how the use of within-study meta-analysis can improve many empirical studies typical of ecology and evolution.
Journal Article