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Cited by 213Meta-analysis and systematic reviewsData Analysis with RStatistical Methods and Bayesian Inference

meta: General Package for Meta-Analysis

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Schwarzer, Guido·2006·CRAN: Contributed Packages
DOI 10.32614/cran.package.meta
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Abstract

User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <doi:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - common effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - three-level meta-analysis model; - generalised linear mixed model; - logistic regression with penalised likelihood for rare events; - Hartung-Knapp method for random effects model; - Kenward-Roger method for random effects model; - prediction interval; - statistical tests for funnel plot asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; - meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; - import data from 'RevMan 5'; - produce forest plot summarising several (subgroup) meta-analyses.

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  • ApoE4 fragments have been found in brains of AD patients.

    “ApoE4 fragments have been found in brains of AD patients [27].”

    From Investigation of the independent role of a rare APOE variant (L28P; APOE*4Pittsburgh) in late-onset Alzheimer disease (2023) · cites this paper as [27]

    The Description and Fleiss1993bin sections (page unavailable) discuss an aspirin meta-analysis example, not ApoE4 or Alzheimer’s disease. Searches for the quoted claim and for apolipoprotein E4 fragments in Alzheimer’s brains found no relevant passage in our copy; the copy is truncated. (LLM, conf=0.72)

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  • WT ApoE4 is more susceptible to proteolysis than the other ApoE isoforms (E2 and E3).

    “WT ApoE4 is more susceptible to proteolysis than the other ApoE isoforms (E2 and E3)”

    From Investigation of the independent role of a rare APOE variant (L28P; APOE*4Pittsburgh) in late-onset Alzheimer disease (2023) · cites this paper as [27]

    The copy’s Argument and metabin Details sections discuss meta-analysis methods, not ApoE isoforms or proteolysis. Searches for ApoE4 susceptibility and proteolytic cleavage relative to ApoE2 and ApoE3 found no relevant passage; the copy is truncated. (LLM, conf=0.72)

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Research works printed but not linked: 32

  1. Deeks JJ, Macaskill P, Irwig L (2005): The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. Journal of Clinical Epidemiology, 58:882-93
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  7. Review Manager (RevMan) [Computer program]. Version 5.
  8. Viechtbauer W (2010): Conducting meta-analyses in R with the metafor package. Journal of Sta- tistical Software, 36, 1-48
  9. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009): Introduction to Meta-Analysis. Chichester: Wiley
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  13. Review Manager (RevMan) [Computer program]. Version 5.4. The Cochrane Collaboration, 2020 See Also meta-package, rob, metabin, metacont, metagen, read.cdir, read.rm5, settings.meta Examples # Locate export data file "Fleiss1993_CR.csv" # in sub-directory of package "meta" # filename <-system.file("extdata", "Fleiss1993_CR.csv", package = "meta") # Fleiss1993_CR <-read.rm5(filename) # Choose RevMan 5 settings and store old settings # oldset <-settings.meta("revman5", quietly = FALSE) # Same result as R command example(Fleiss1993bin) # metacr(Fleiss1993_CR) # Same result as R command example(Fleiss1993cont) # metacr(Fleiss1993_CR, 1, 2) forest(metacr(Fleiss1993_CR, 1, 2)) # Change summary measure to RR # m1 <-metacr(Fleiss1993_CR) update(m1, sm="RR") # Use old settings # settings.meta(oldset) metacum.meta Cumulative meta-analysis Description Performs a cumulative meta-analysis. References
  14. Viechtbauer W (2010): Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36, 1-48
  15. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T (2013): Meta-analysis of prevalence. Journal of Epidemiology and Community Health, 67, 974-8
  16. Freeman MF & Tukey JW (1950): Transformations related to the angular and the square root. Annals of Mathematical Statistics, 21, 607-11
  17. Viechtbauer W (2010): Conducting meta-analyses in R with the metafor package. Journal of Sta- tistical Software, 36, 1-48
  18. Freeman MF & Tukey JW (1950): Transformations related to the angular and the square root. Annals of Mathematical Statistics, 21, 607-11
  19. Altman DG (1998): Confidence intervals for the number needed to treat. British Medical Journal, 317, 1309-12
  20. Altman DG, Andersen PK (1999): Calculating the number needed to treat for trials where the outcome is time to an event. British Medical Journal, 319, 1492-95
  21. Cook RJ, Sackett DL (1995): The number needed to treat: a clinically useful measure of treatment effect. British Medical Journal, 310, 452-54
  22. Laupacis A, Sackett DL, Roberts RS (1988): An assessment of clinically useful measures of the consequences of treatment. New England Journal of Medicine, 318, 1728-33
  23. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009): Introduction to Meta-Analysis. Chichester: Wiley
  24. See Also metacr, rob, read.rm5 Examples # Locate file "Fleiss1993.zip" with Cochrane data package in # sub-directory of R package meta # filename <-system.file("extdata/Fleiss1993.zip", package = "meta") Fleiss1993_CR <-read.cdir(filename) Fleiss1993_CR # Same result as R Command example(Fleiss1993bin): # metacr(Fleiss1993_CR) # Same result as R Command example(Fleiss1993cont): # metacr(Fleiss1993_CR, 1, 2) References Review Manager (RevMan) [Computer program]. Version 4.2. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2003 References Review Manager (RevMan) [Computer program]. Version 5.4. The Cochrane Collaboration, 2020 See Also summary.rm5, metabias.rm5, metabin, metacont, metagen, metacr, print.rm5 Examples # Locate export data file "Fleiss1993_CR.csv" # in sub-directory of package "meta" # filename <-system.file("extdata", "Fleiss1993_CR.csv", package = "meta") Fleiss1993_CR <-read.rm5(filename) # Same result as R command example(Fleiss1993bin): # metacr(Fleiss1993_CR) • RoB 1 tool for randomized studies (RoB1): 1. Random sequence generation (selection bias) 2. Allocation concealment (selection bias) 3. Blinding of participants and personnel (performance bias) 4. Blinding of outcome assessment (detection bias) 5. Incomplete outcome data (attrition bias) 6. Selective reporting (reporting bias) 7. Other bias • RoB 2 tool for randomized studies (RoB2): 1. Bias arising from the randomization process 2. Bias due to deviations from intended intervention" 3. Bias due to missing outcome data 4. Bias in measurement of the outcome 5. Bias in selection of the reported result • RoB 2 tool for cluster-randomized trials (RoB2-cluster): 1. Bias arising from the randomization process 2. Bias arising from the identification or recruitment of participants into clusters 3. Bias due to deviations from intended intervention 4. Bias due to missing outcome data 5. Bias in measurement of the outcome 6. Bias in selection of the reported result • RoB 2 tool for crossover trials (RoB2-crossover) 1. Bias arising from the randomization process 2. Bias arising from period and carryover effects 3. Bias due to deviations from intended intervention 4. Bias due to missing outcome data 5. Bias in measurement of the outcome 6. Bias in selection of the reported result • Risk Of Bias In Non-randomized Studies -of Intervention (ROBINS-I): 1. Risk of bias due to confounding 2. Risk of bias in selection of participants into the study References
  25. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD et al. (2011): The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. British Medical Journal, 343: d5928
  26. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. (2016): ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. British Medical Journal, 355: i4919
  27. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. (2019): RoB 2: a revised tool for assessing risk of bias in randomised trials. British Medical Journal, 366: l4898.
  28. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009): Introduction to Meta-Analysis. Chichester: Wiley
  29. Cox DR, Snell EJ (1989): Analysis of Binary Data (2nd edition). London: Chapman and Hall / CRC Hasselblad V, Hedges LV (1995): Meta-analysis of screening and diagnostic tests. Psychological Bulletin, 117, 167-78
  30. Higgins JPT & Thompson SG (2002): Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539-58 See Also print.summary.meta, metabin, metacont, metagen Examples data(Fleiss1993cont) m1 <-metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont, data = Fleiss1993cont, studlab = paste(study, year), sm = "SMD") summary(m1) summary(update(m1, subgroup = c(1, 2, 1, 1, 2), subgroup.name = "group")) forest(update(m1, subgroup = c(1, 2, 1, 1, 2), subgroup.name = "group")) ## Not run: # Use unicode characters to print tau^2, tau, and I^2 print(summary(m1), text.tau2 = "\u03c4\u00b2", text.tau = "\u03c4", text.I2 = "I\u00b2") ## End(Not run) summary.rm5 Cochrane review: detailed summary of meta-analyses Description Calculate and print a detailed summary of all meta-analyses in a Cochrane review. Usage ## S3 method for class 'rm5' summary(object, comp.no, outcome.no, ...) ## S3 method for class 'cdir' summary(object, comp.no, outcome.no, ...) ## S3 method for class 'summary.rm5' print(x, ...) References
  31. Duval S & Tweedie R (2000a): A nonparametric "Trim and Fill" method of accounting for publica- tion bias in meta-analysis. Journal of the American Statistical Association, 95, 89-98
  32. Schwarzer G, Carpenter J, Rücker G (2010): Empirical evaluation suggests Copas selection model preferable to trim-and-fill method for selection bias in meta-analysis Journal of Clinical Epidemi- ology, 63, 282-8

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Authors
Schwarzer, G.
Corresponding author
Schwarzer, Guido
ORCID
Schwarzer, Guido
Venue
CRAN: Contributed Packages
Publisher
The R Foundation
Published
2006-02-08
DOI
10.32614/cran.package.meta
OpenAlex
W4399649028
Cited by
202
References
2
Citations by year
2026: 40 · 2025: 35 · 2024: 21 · 2023: 17 · 2022: 20 · 2021: 16 · 2020: 15 · 2019: 11 · 2018: 11 · 2017: 9 · 2016: 7
Type
dataset
Language
en
Open access
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
meta-analysis, R package, common effect model, random effects models, forest plots, funnel plot, generalized linear mixed models, logistic regression, rare events, prediction intervals, funnel plot asymmetry, trim and fill, meta-regression, cumulative meta-analysis

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