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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2020

Open Access 01-12-2020 | Research article

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

Authors: Michael Kossmeier, Ulrich S. Tran, Martin Voracek

Published in: BMC Medical Research Methodology | Issue 1/2020

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Abstract

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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Literature
1.
Tukey JW. Exploratory data analysis. Reading: Addison-Wesley; 1977.
2.
Tufte ER. The visual display of quantitative information. 2nd ed. Cheshire: Graphics Press; 2001.
3.
Krause A, O’Connell M. A picture is worth a thousand tables: graphics in life sciences. New York: Springer; 2012.CrossRef
4.
Chen C-H, Härdle WK, Unwin A. Handbook of data visualization. Berlin: Springer; 2008.CrossRef
5.
Glass GV. Primary, secondary, and meta-analysis of research. Educ Res. 1976;5:3–8.CrossRef
6.
Glass GV. Integrating findings: the meta-analysis of research. Rev Educ Res. 1977;5:351–79.
7.
Light RJ, Singer JD, Willet JB. The visual presentation and interpretation of meta-analyses. In: Cooper H, Hedges LV, editors. The handbook of research synthesis. New York: Russell Sage; 1994. p. 439–53.
8.
DuMouchel W, Normand S-L. Computer-modeling and graphical strategies for meta-analysis. In: Stangl DK, Berry DA, editors. Meta-analysis in medicine and health policy. New York: Marcel Dekker; 2000. p. 127–78.
9.
Borman GD, Grigg JA. Visual and narrative interpretation. In: Cooper H, Hedges LV, Valentine JC, editors. The handbook of research synthesis and meta-analysis. 2nd ed. New York: Russell Sage; 2009. p. 497–519.
10.
Bax L, Ikeda N, Fukui N, Yaju Y, Tsuruta H, Moons KG. More than numbers: the power of graphs in meta-analysis. Am J Epidemiol. 2009;169:249–55.PubMedCrossRef
11.
Anzures-Cabrera J, Higgins JP. Graphical displays for meta-analysis: an overview with suggestions for practice. Res Synth Methods. 2010;1:66–80.PubMedCrossRef
12.
Kiran A, Crespillo AP, Rahimi K. Graphics and statistics for cardiology: data visualisation for meta-analysis. Heart. 2017;103:19–23.PubMedCrossRef
13.
Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol. 2011;64:163–71.PubMedCrossRef
14.
15.
Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Chichester: Wiley; 2009.CrossRef
16.
The Cochrane Collaboration. Review Manager (RevMan) (Version 5.3) [Computer program]. Copenhagen: The Nordic Cochrane Centre; 2014.
17.
Bax L, Yu LM, Ikeda N, Tsuruta H, Moons KG. Development and validation of MIX: comprehensive free software for meta-analysis of causal research data. BMC Med Res Methodol. 2006;6:50.PubMedPubMedCentralCrossRef
18.
Palmer TM, Sterne JAC, editors. Meta-analysis in Stata: an updated collection from the Stata journal. 2nd ed. College Station: Stata Press; 2015.
19.
20.
21.
22.
Schild AH, Voracek M. Less is less: a systematic review of graph use in meta-analyses. Res Synth Methods. 2013;4:209–19.PubMed
23.
Schild AH, Voracek M. Finding your way out of the forest without a trail of bread crumbs: development and evaluation of two novel displays of forest plots. Res Synth Methods. 2015;6:74–86.PubMedCrossRef
24.
Ogilvie D, Fayter D, Petticrew M, Sowden A, Thomas S, Whitehead M, Worthy G. The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Med Res Methodol. 2008;8:1.CrossRef
25.
Krahn U, Binder H, König J. A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol. 2013;13:1.CrossRef
26.
Krahn U, Binder H, König J. Visualizing inconsistency in network meta-analysis by independent path decomposition. BMC Med Res Methodol. 2014;1:131.CrossRef
27.
Smith ML, Glass GV, Miller TI. The benefits of psychotherapy. Baltimore: Johns Hopkins University Press; 1980.
28.
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network meta-analysis for decision-making. Chichester: Wiley-Blackwell; 2018.CrossRef
29.
Barrowman NJ, Myers RA. Raindrop plots: a new way to display collections of likelihoods and distributions. Am Stat. 2003;57:268–74.CrossRef
30.
Sutton AJ, Cooper NJ, Jones DR, Lambert PC, Thompson JR, Abrams KR. Evidence-based sample size calculations based upon updated meta-analysis. Stat Med. 2007;26:2479–500.PubMedCrossRef
31.
32.
33.
34.
Thompson CG. Graphing effects as fuzzy numbers in meta-analysis. J Mod Appl Stat Methods. 2016;15:49.CrossRef
35.
Light RJ, Pillemer DB. Summing up: the science of reviewing research. Cambridge: Harvard University Press; 1984.
36.
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Br Med J. 1997;315:629–34.CrossRef
37.
Elvik R. Evaluating the statistical conclusion validity of weighted mean results in meta-analysis by analysing funnel graph diagrams. Accid Anal Prev. 1998;30:255–66.PubMedCrossRef
38.
Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56:455–63.PubMedCrossRef
39.
Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J Clin Epidemiol. 2008;61:991–6.PubMedCrossRef
40.
Langan D, Higgins JP, Gregory W, Sutton AJ. Graphical augmentations to the funnel plot to assess the impact of additional evidence on a meta-analysis. J Clin Epidemiol. 2012;65:511–9.PubMedCrossRef
41.
Crowther MJ, Langan D, Sutton AJ. Graphical augmentations to the funnel plot to assess the impact of additional evidence on a meta-analysis. Stata J. 2012;12:605–22.CrossRef
42.
Chevance A, Schuster T, Steele R, Ternès N, Platt RW. Contour plot assessment of existing meta-analyses confirms robust association of statin use and acute kidney injury risk. J Clin Epidemiol. 2015;68:1138–43.PubMedPubMedCentralCrossRef
43.
44.
45.
Galbraith RF. A note on graphical presentation of estimated odds ratios from several clinical trials. Stat Med. 1988;7:889–94.PubMedCrossRef
46.
Gee T. Capturing study influence: the concept of ‘gravity’ in meta-analysis. Aust Couns Res J. 2005;1:52–75.
47.
Barendregt JJ, Doi S. MetaXL User Guide. Sunrise Beach: EpiGear International; 2016.
48.
Lau J, Ioannidis JP, Schmid CH. Summing up evidence: one answer is not always enough. Lancet. 1998;351:123–7.PubMedCrossRef
49.
Weinhandl ED, Duval S. Generalization of trim and fill for application in meta-regression. Res Synth Methods. 2012;3:51–67.PubMedCrossRef
50.
Dusseldorp E, Van Genugten L, van Buuren S, Verheijden MW, van Empelen P. Combinations of techniques that effectively change health behavior: evidence from Meta-CART analysis. Health Psychol. 2014;33:1530–40.PubMedCrossRef
51.
van Lissa CJ. metaforest [R package software]. 2017. S
52.
Wang XV, Cole B, Bonetti M, Gelber RD. Meta-STEPP: subpopulation treatment effect pattern plot for individual patient data meta-analysis. Stat Med. 2016;35:3704–16.PubMedCrossRef
53.
Thompson SG. Controversies in meta-analysis: the case of the trials of serum cholesterol reduction. Stat Methods Med Res. 1993;2:173–92.PubMedCrossRef
54.
Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Methods for meta-analysis in medical research. Chichester: Wiley; 2000.
55.
Baujat B, Mahé C, Pignon JP, Hill C. A graphical method for exploring heterogeneity in meta-analyses: application to a meta-analysis of 65 trials. Stat Med. 2002;21:2641–52.PubMedCrossRef
56.
57.
Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1:112–25.PubMedCrossRef
58.
Poorolajal J, Mahmoodi M, Majdzadeh R, Fotouhi A. Metaplot: a novel stata graph for assessing heterogeneity at a glance. Iran J Public Health. 2010;39:102–4.PubMedPubMedCentral
59.
Olkin I, Dahabreh IJ, Trikalinos TA. GOSH: a graphical display of study heterogeneity. Res Synth Methods. 2012;3:214–23.PubMedCrossRef
60.
Beath KJ. A finite mixture method for outlier detection and robustness in meta-analysis. Res Synth Methods. 2014;5:285–93.PubMedCrossRef
61.
Mavridis D, Moustaki I, Wall M, Salanti G. Detecting outlying studies in meta-regression models using a forward search algorithm. Res Synth Methods. 2017;8:199–211.PubMedCrossRef
62.
63.
Kulinskaya E, Koricheva J. Use of quality control charts for detection of outliers and temporal trends in cumulative meta-analysis. Res Synth Methods. 2010;1:297–307.PubMedCrossRef
64.
Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC. Cumulative meta-analysis of therapeutic trials for myocardial infarction. N Engl J Med. 1992;327:248–54.PubMedCrossRef
65.
Pogue JM, Yusuf S. Cumulating evidence from randomized trials: utilizing sequential monitoring boundaries for cumulative meta-analysis. Control Clin Trials. 1997;18:580–93.PubMedCrossRef
66.
Ioannidis JP, Contopoulos-Ioannidis DG, Lau J. Recursive cumulative meta-analysis: a diagnostic for the evolution of total randomized evidence from group and individual patient data. J Clin Epidemiol. 1999;52:281–91.PubMedCrossRef
67.
68.
Scheibehenne B, Jamil T, Wagenmakers EJ. Bayesian evidence synthesis can reconcile seemingly inconsistent results: the case of hotel towel reuse. Psychol Sci. 2016;27:1043–6.PubMedCrossRef
69.
70.
Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36:1–48.CrossRef
71.
van Houwelingen HC, Zwinderman KH, Stijnen T. A bivariate approach to meta-analysis. Stat Med. 1993;12:2273–84.PubMedCrossRef
72.
Wang MC, Bushman BJ. Using the normal quantile plot to explore meta-analytic data sets. Psychol Methods. 1998;3:46–54.CrossRef
73.
Higgins JP, Green S, editors. Cochrane handbook for systematic reviews of interventions. Chichester: Wiley; 2008.
74.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–9.PubMedCrossRef
75.
Panesar SS, Rao C, Vecht JA, Mirza SB, Netuveli G, Morris R, et al. Development of the Veritas plot and its application in cardiac surgery: an evidence-synthesis graphic tool for the clinician to assess multiple meta-analyses reporting on a common outcome. Can J Surg. 2009;52:E137–45.PubMedPubMedCentral
76.
Keus F, Wetterslev J, Gluud C, van Laarhoven CJ. Evidence at a glance: error matrix approach for overviewing available evidence. BMC Med Res Methodol. 2010;10:1.CrossRef
77.
Thomson HJ, Thomas S. The effect direction plot: visual display of non-standardised effects across multiple outcome domains. Res Synth Methods. 2013;4:95–101.PubMedCrossRef
78.
Wang DD, Shams-White M, Bright OJM, Parrott JS, Chung M. Creating a literature database of low-calorie sweeteners and health studies: evidence mapping. BMC Med Res Methodol. 2016;16:1.PubMedPubMedCentralCrossRef
79.
Hanji MB. Meta-analysis in psychiatry research: fundamental and advanced methods. Toronto: Apple Academic Press; 2017.CrossRef
80.
L’Abbé KA, Detsky AS, O’Rourke K. Meta-analysis in clinical research. Ann Intern Med. 1987;107:224–33.PubMedCrossRef
81.
Jimenez FJ, Guallar E, Martín-Moreno JM. A graphical display useful for meta-analysis. Eur J Pub Health. 1997;7:101–5.CrossRef
82.
Voest EE, van Houwelingen JC, Neijt JP. A meta-analysis of prognostic factors in advanced ovarian cancer with median survival and overall survival (measured with the log (relative risk)) as main objectives. Eur J Cancer. 1989;25:711–20.CrossRef
83.
Brannick MT, Gültaş M. Cross-hairs: a scatterplot for meta-analysis in R. Res Synth Methods. 2017;8:53–63.PubMedCrossRef
84.
Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat Med. 1993;12:1293–316.PubMedCrossRef
85.
Phillips B, Stewart LA, Sutton AJ. ‘Cross hairs’ plots for diagnostic meta-analysis. Res Synth Methods. 2010;1:308–15.PubMedCrossRef
86.
Deeks JJ, Altman DG. Effect measures for meta-analysis of trials with binary outcomes. In: Egger M, Smith GD, Altman DG, editors. Systematic reviews in health care: meta-analysis in context. 2nd ed. London: BMJ Books; 2001. p. 313–35.CrossRef
87.
Olliaro P, Vaillant MT. Alternative visual displays of metaanalysis of malaria treatment trials to facilitate translation of research into policy. Diagn Microbiol Infect Dis. 2010;68:422–31.PubMedCrossRef
88.
Steinhauser S, Schumacher M, Rücker G. Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies. BMC Med Res Methodol. 2016;16:97.PubMedPubMedCentralCrossRef
89.
Shadish WR, Sweeney RB. Mediators and moderators in meta-analysis: there’s a reason we don’t let dodo birds tell us which psychotherapies should have prizes. J Consult Clin Psychol. 1991;59:883–93.PubMedCrossRef
90.
Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 2002;62:4427–33.PubMed
91.
Choi JK, Yu U, Kim S, Yoo OJ. Combining multiple microarray studies and modeling interstudy variation. Bioinformatics. 2003;19:i84–90.PubMedCrossRef
92.
Hattie J. Visible learning: a synthesis of over 800 meta-analyses relating to achievement. London: Routledge; 2008.CrossRef
93.
Bowden J, Tierney JF, Copas AJ, Burdett S. Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics. BMC Med Res Methodol. 2011;11:41.PubMedPubMedCentralCrossRef
94.
Feichtinger J, McFarlane RJ, Larcombe LD. CancerMA: a web-based tool for automatic meta-analysis of public cancer microarray data. Database. 2012;2012:bas055.PubMedPubMedCentralCrossRef
95.
Gartlehner G, Schultes MT, Titscher V, Morgan LC, Bobashev GV, Williams P, West SL. User testing of an adaptation of fishbone diagrams to depict results of systematic reviews. BMC Med Res Methodol. 2017;17:169.PubMedPubMedCentralCrossRef
96.
Babatunde OO, Tan V, Jordan JL, Dziedzic K, Chew-Graham CA, Jinks C, et al. Evidence flowers: an innovative, visual method of presenting “best evidence” summaries to health professional and lay audiences. Res Synth Methods. 2018;9:273–84.PubMedCrossRef
97.
Schweder T, Spjøtvoll E. Plots of p-values to evaluate many tests simultaneously. Biometrika. 1982;69:493–502.CrossRef
98.
Dear KB, Begg CB. An approach for assessing publication bias prior to performing a meta-analysis. Stat Sci. 1992;7:237–45.CrossRef
99.
Copas J, Shi JQ. Meta-analysis, funnel plots and sensitivity analysis. Biostatistics. 2000;1:247–62.PubMedCrossRef
100.
Ioannidis JP, Trikalinos TA. An exploratory test for an excess of significant findings. Clin Trials. 2007;4:245–53.PubMedCrossRef
101.
Gerber AS, Malhotra N. Publication bias in empirical sociological research: do arbitrary significance levels distort published results? Sociol Methods Res. 2008;37:3–30.CrossRef
102.
Formann AK. Estimating the proportion of studies missing for meta-analysis due to publication bias. Contemp Clin trials. 2008;29:732–9.PubMedCrossRef
103.
104.
Simonsohn U, Nelson LD, Simmons JP. P-curve: a key to the file-drawer. J Exp Psychol Gen. 2014;143:534–47.PubMedCrossRef
105.
Taylor AE, Munafò MR. Triangulating meta-analyses: the example of the serotonin transporter gene, stressful life events and major depression. BMC Psychol. 2016;4:23.PubMedPubMedCentralCrossRef
106.
Schwarzer G, Carpenter JR, Rücker G. Meta-analysis with R. Cham: Springer; 2015.CrossRef
107.
Harrison S, Jones HE, Martin RM, Lewis SJ, Higgins JP. The albatross plot: a novel graphical tool for presenting results of diversely reported studies in a systematic review. Res Synth Methods. 2017;8:281–9.PubMedPubMedCentralCrossRef
108.
109.
König J, Krahn U, Binder H. Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Stat Med. 2013;32:5414–29.PubMedCrossRef
110.
Batson S, Score R, Sutton AJ. Three-dimensional evidence network plot system: covariate imbalances and effects in network meta-analysis explored using a new software tool. J Clin Epidemiol. 2017;86:182–95.PubMedCrossRef
111.
Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol. 2008;61:455–63.PubMedCrossRef
112.
Tan SH, Cooper NJ, Bujkiewicz S, Welton NJ, Caldwell DM, Sutton AJ. Novel presentational approaches were developed for reporting network meta-analysis. J Clin Epidemiol. 2014;67:672–80.PubMedCrossRef
113.
Phillippo DM, Dias S, Ades AE, Didelez V, Welton NJ. Sensitivity of treatment recommendations to bias in network meta-analysis. J R Stat Soc Ser A Stat Soc. 2018;181:843–67.PubMedCrossRef
114.
Salanti G, Kavvoura FK, Ioannidis JP. Exploring the geometry of treatment networks. Ann Intern Med. 2008;148:544–53.PubMedCrossRef
115.
Chung H, Lumley T. Graphical exploration of network meta-analysis data: the use of multidimensional scaling. Clin Trials. 2008;5:301–7.PubMedCrossRef
116.
Hawkins N, Scott DA, Woods BS, Thatcher N. No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data. Value Health. 2009;12:996–1003.PubMedCrossRef
117.
Naci H. Communication of treatment rankings obtained from network meta-analysis using data visualization. Circ Cardiovasc Qual Outcomes. 2016;9:605–8.PubMedCrossRef
118.
Salanti G, Marinho V, Higgins JP. A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J Clin Epidemiol. 2009;62:857–64.PubMedCrossRef
119.
Cipriani A, Furukawa TA, Salanti G, Geddes JR, Higgins JP, Churchill R, et al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet. 2009;373:746–58.PubMedCrossRef
120.
Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med. 2009;28:1861–81.PubMedCrossRef
121.
Donegan S, Dias S, Tudur-Smith C, Marinho V, Welton NJ. Graphs of study contributions and covariate distributions for network meta-regression. Res Synth Methods. 2018;9:243–60.PubMedPubMedCentralCrossRef
122.
Senn S, Gavini F, Magrez D, Scheen A. Issues in performing a network meta-analysis. Stat Methods Med Res. 2013;22:169–89.PubMedCrossRef
123.
Salanti G, Del Giovane C, Chaimani A, Caldwell DM, Higgins JP. Evaluating the quality of evidence from a network meta-analysis. PLoS One. 2014;9:e99682.PubMedPubMedCentralCrossRef
124.
Veroniki AA, Straus SE, Fyraridis A, Tricco AC. The rank-heat plot is a novel way to present the results from a network meta-analysis including multiple outcomes. J Clin Epidemiol. 2016;76:193–9.PubMedCrossRef
125.
Rücker G, Schwarzer G. Resolve conflicting rankings of outcomes in network meta-analysis: partial ordering of treatments. Res Synth Methods. 2017;8:526–36.PubMedCrossRef
126.
127.
Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391:1357–66.PubMedPubMedCentralCrossRef
128.
Freiman JA, Chalmers TC, Smith H Jr, Kuebler RR. The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial: survey of 71 negative trials. N Engl J Med. 1978;299:690–4.PubMedCrossRef
129.
Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. Br Med J. 2001;322:1479–80.CrossRef
130.
Lakens D, Scheel AM, Isager PM. Equivalence testing for psychological research: a tutorial. Adv Methods Pract Psychol Sci. 2018;1:259–69.CrossRef
131.
Voracek M, Kossmeier M, Tran US. Which data to meta-analyze, and how? A specification-curve and multiverse-analysis approach to meta-analysis. Z Psychol. 2019;227:64–82.
132.
APA Publications and Communications Board Working Group on Journal Article Reporting Standards. Reporting standards for research in psychology: why do we need them? What might they be? Am Psychol. 2008;63:839–51.PubMedCentralCrossRef
133.
Cooper H, Hedges LV, Valentine JC, editors. The handbook of research synthesis and meta-analysis. 3rd ed. New York: Russell Sage; 2019.
134.
135.
Tay L, Parrigon S, Huang Q, LeBreton JM. Graphical descriptives: a way to improve data transparency and methodological rigor in psychology. Perspect Psychol Sci. 2016;11:692–701.PubMedCrossRef
Metadata
Title
Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis
Authors
Michael Kossmeier
Ulrich S. Tran
Martin Voracek
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2020
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-020-0911-9

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