How trace plots help interpret meta-analysis results.
best linear unbiased prediction (BLUP)
meta-analysis
random-effects model
shrinkage
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
15 Dec 2023
15 Dec 2023
Historique:
revised:
30
11
2023
received:
29
06
2023
accepted:
04
12
2023
medline:
15
12
2023
pubmed:
15
12
2023
entrez:
15
12
2023
Statut:
aheadofprint
Résumé
The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : FR 3070/3-1
Informations de copyright
© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
Références
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. John Wiley & Sons; 2009.
Friede T, Röver C, Wandel S, Neuenschwander B. Meta-analysis of few small studies in orphan diseases. Res Synth Methods. 2017;8(1):79-91. doi:10.1002/jrsm.1217
Jackson D, White IR. When should meta-analysis avoid making hidden normality assumptions? Biom J. 2018;60(6):1040-1058. doi:10.1002/bimj.201800071
Raudenbush SW, Bryk AS. Empirical Bayes meta-analysis. J Educ Behav Statis. 1985;10(2):75-98. doi:10.3102/10769986010002075
Anzures-Cabrera J, Higgins JPT. Graphical displays for meta-analysis: an overview with suggestions for practice. Res Synth Methods. 2010;1(1):66-80. doi:10.1002/jrsm.6
Kossmeier M, Tran US, Voracek M. Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis. BMC Med Res Methodol. 2020;20:26. doi:10.1186/s12874-020-0911-9
Nikolakopoulou A, Chaimani A. More than words: novel visualizations for evidence synthesis. Res Synth Methods. 2021;12(1):2-3. doi:10.1002/jrsm.1472
Rubin DB. Estimation in parallel randomized experiments. J Educ Statis. 1981;6(4):377-401. doi:10.3102/10769986006004377
DuMouchel W. Hierarchical Bayes Linear Models for Meta-Analysis. Technical report 27, National Institute of Statistical Sciences (NISS); Research Triangle Park, NC. 1994 https://www.niss.org/research/technical-reports/hierarchical-bayes-linear-models-meta-analysis-1994
DuMouchel W. Availability of hblm S-PLUS software. Internet Archive. 1997 https://web.archive.org/web/19970617085954/http://www.research.att.com/~dumouchel/bsoft.html
Gaver DP, Draper D, Goel PK, et al. Combining Information: Statistical Issues and Opportunities for Research. National Research Council, The National Academies Press; 1992 https://nap.nationalacademies.org/20865
Gelman A, Carlin JB, Stern H, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. 3rd ed. Chapman & Hall/CRC; 2014.
DuMouchel WH, Normand SL. Computer modeling strategies for meta-analysis. In: Stangl DK, Berry DA, eds. Meta-Analysis in Medicine and Health Policy. Marcel Dekker; 2000:127-178.
Zucker DR, Schmid CH, McIntosh MW, D'Agustino RB, Selker HP, Lau J. Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment. J Clin Epidemiol. 1997;50(4):401-410. doi:10.1016/S0895-4356(96)00429-5
Shadish WR, Rindskopf DM, Hedges LV, Sullivan KJ. Bayesian estimates of autocorrelations in single-case designs. Behav Res Methods. 2013;45(3):813-821. doi:10.3758/s13428-012-0282-1
Inselberg A. The plane with parallel coordinates. Vis Comput. 1985;1(4):69-91. doi:10.1007/BF01898350
Efron B, Morris C. Data analysis using Stein's estimator and its generalizations. J Am Stat Assoc. 1975;70(350):311-319. doi:10.2307/2285814
Röver C. Bayesian random-effects meta-analysis using the bayesmeta R package. J Stat Softw. 2020;93(6):1-51. doi:10.18637/jss.v093.i06
Röver C, Friede T. Using the bayesmeta R package for Bayesian random-effects meta-regression. Comput Methods Programs Biomed. 2023;229:107303. doi:10.1016/j.cmpb.2022.107303
Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1-48. doi:10.18637/jss.v036.i03
Röver C, Bender R, Dias S, et al. On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis. Res Synth Methods. 2021;12(4):448-474. doi:10.1002/jrsm.1475
Röver C, Friede T. Bounds for the weight of external data in shrinkage estimation. Biom J. 2021;65(5):1131-1143. doi:10.1002/bimj.202000227
Bland JM, Altman DG. Regression towards the mean. BMJ. 1994;308:1499. doi:10.1136/bmj.308.6942.1499
Bland JM, Altman DG. Some examples of regression towards the mean. BMJ. 1994;309:780. doi:10.1136/bmj.309.6957.780
Röver C, Friede T. Dynamically borrowing strength from another study through shrinkage estimation. Stat Methods Med Res. 2020;29(1):293-308. doi:10.1177/0962280219833079
Wandel S, Neuenschwander B, Röver C, Friede T. Using phase II data for the analysis of phase III studies: an application in rare diseases. Clin Trials. 2017;14(3):277-285. doi:10.1177/1740774517699409
Peto R. Aspirin after myocardial infarction. The Lancet. 1980;315(8179):1172-1173.
Canner P. An overview of six clinical trials of aspirin in coronary heart disease. Stat Med. 1987;6(3):255-263. doi:10.1002/sim.4780060310
Hasselblad V, Eddy DM, Kotchmar DJ. Synthesis of environmental evidence: nitrogen dioxide epidemiology studies. J Air Waste Manage Assoc. 1992;42(5):662-671. doi:10.1080/10473289.1992.10467018
Higgins JPT, López-López JA, Aloe AM. Meta-regression. In: Schmid CH, White I, Stijnen T, eds. Handbook of Meta-analysisNew. Chapman and Hall/CRC; 2021.
Higgins JPT, Thomas J, Chandler J, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Wiley & Sons; 2019.
Rubin DB. Meta-analysis: literature synthesis or effect-size surface estimation? J Educ Statis. 1992;17(4):363-374. doi:10.2307/1165129
Karner C, Chong J, Poole P. Tiotropium versus placebo for chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2014;7:CD009285. doi:10.1002/14651858.CD009285.pub3
Röver C, Andreas S, Friede T. Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data. Biom J. 2016;58(1):170-185. doi:10.1002/bimj.201300288
Doherty DE. A review of the role of FEV1 in the COPD paradigm. COPD: J Chron Obstruct Pulmon Dis. 2008;5(5):310-318. doi:10.1080/15412550802363386
Robinson GK. That BLUP is a good thing: the estimation of random effects. Statis Sci. 1991;6(1):15-32. doi:10.1214/ss/1177011926
Aitkin M, Liu C. Confidence, credibility and prediction. Metron. 2018;76:251-268. doi:10.1007/s40300-018-0139-1
Morey RD, Hoekstra R, Rouder JN, Lee MD, Wagenmakers EJ. The fallacy of placing confidence in confidence intervals. Psychon Bull Rev. 2016;23(1):103-123. doi:10.3758/s13423-015-0947-8
Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Statis. 2005;30(3):261-293. doi:10.3102/10769986030003261
Viechtbauer W. Confidence intervals for the amount of heterogeneity in meta-analysis. Stat Med. 2007;26(1):37-52. doi:10.1002/sim.2514
Röver C, Knapp G, Friede T. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Med Res Methodol. 2015;15:15. doi:10.1186/s12874-015-0091-1
Röver C. bayesmeta: Bayesian random-effects meta analysis. R package. http://cran.r-project.org/package=bayesmeta 2015.
Balduzzi S, Rücker G, Nikolakopoulou A, et al. netmeta: an R package for network meta-analysis using frequentist methods. J Stat Softw. 2023;106(2):1-40. doi:10.18637/jss.v106.i02
White T, Noble D, Senior A, Hamilton WK, Viechtbauer W. metadat: Meta-Analysis Datasets. R package, version 1.3-0. https://github.com/wviechtb/metadat 2023.