Graphing Effects as Fuzzy Numbers in Meta-Analysis

Thompson, Christopher W.

doi:10.22237/jmasm/1462078080

Cited by 3 publications

(4 citation statements)

References 14 publications

(10 reference statements)

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“…For examples of fuzzy number plots, see the figures in the work by Thompson. 26 Bender et al 12 also discussed the scaling of the y-axis and recommended an inverse (top-down) scale for the y-axis, however, they did not consider our linearization transformation. Another potential transformation of the y-axis is the double-square root transformation which also emphasizes smaller P-values and has been used, for example, to compare P-values from different metaanalysis models.…”

Section: Discussionmentioning

confidence: 99%

“…Thompson suggested a very similar plot based on a completely different consideration in the framework of Fuzzy Set Theory, called “fuzzy number plot,” 26 see also Kossmeier et al 27 Here, the y‐axis represents the “membership grade,” in this framework meaning the grade (between 0 and 1) to which an element belongs to a (fuzzy) set. Grade 1 means that the fuzzy set reduces to a one‐point interval of length 0, here the observed point estimate.…”

Section: Discussionmentioning

confidence: 99%

“…However, the membership grade is just a linear transformation of the test statistic, and we think the test statistic a concept more widely known to statisticians. For examples of fuzzy number plots, see the figures in the work by Thompson 26 …”

Section: Discussionmentioning

confidence: 99%

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Beyond the forest plot: The drapery plot

Rücker

Schwarzer

2020

Research Synthesis Methods

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In the era of the "reproducibility crisis" and the "P-value controversy" new ways of presentation and interpretation of the results of a meta-analysis are desirable. One suggestion that has been made for single studies almost six decades ago and taken up now and then is the P-value function. For a given outcome, this function assigns a P-value to each possible hypothetical value, given the data. Moreover, the P-value function simultaneously provides two-sided confidence intervals for all possible alpha levels. An application to meta-analysis, while suggested early, has not been widely established. We introduce the drapery plot that presents the P-value function for all individual studies and pooled estimates in a meta-analysis as curves and the prediction range for a single future study. We also present a scaled variant with the test statistic on the y-axis. Both plots visualize the full information of a pairwise meta-analysis. We see a drapery plot as a complementary figure to a forest plot. It may be even an alternative in meta-analyses with many studies where forest plots tend to become very large and complex.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Beyond the forest plot: The drapery plot

Rücker

Schwarzer

2020

Research Synthesis Methods

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“…Examples of recently proposed variants of the forest plot are the rainforest plot and the thick forest plot, which allocate more visual emphasis on those study effects which have been estimated with higher precision [23]. A novel, rather atypical, forest plot-like display is the fuzzy number plot, which shows study and summary effects and respective interval estimates using fuzzy numbers and which has specifically been proposed for large-scale meta-analyses with numerous studies, for which traditional forest plots are less suited [34].…”

Section: Forest Plot-like Graphical Displaysmentioning

confidence: 99%

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

Kossmeier

Tran

Voracek

2020

BMC Med Res Methodol

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Background: Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable. Methods: We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes. Results: We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features. Conclusions: The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

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Heterogeneity in meta-analysis: a comprehensive overview

Stogiannis

Siannis²,

Androulakis

2023

The International Journal of Biostatistics

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In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.

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Graphing Effects as Fuzzy Numbers in Meta-Analysis

Cited by 3 publications

References 14 publications

Beyond the forest plot: The drapery plot

Beyond the forest plot: The drapery plot

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

Heterogeneity in meta-analysis: a comprehensive overview

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