The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
netmeta : An R Package for Network Meta-Analysis Using Frequentist Methods
Open accessAbstract
Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Effects from small studies (potential publication bias) were assessed through visual inspection of contour-enhanced funnel plots and incorporated into assessments of the certainty of the evidence.36 47”
Citations
Cites
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Cochrane Handbook for Systematic Reviews of Interventions
(2019) · doi:10.1002/9781119536604 · Cited by 14,828
Statistical Aspects of the Analysis of Data From Retrospective Studies of Disease
(1959) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/22.4.719 · Cited by 14,689
Hutton, B., Salanti, G., Caldwell, D. M., Chaimani, A., Schmid, C. H., Cameron, C., Ioannidis, J. P. A., Straus, S., Thorlund, K., Jansen, J. P., et al. (2015) · Annals of Internal Medicine · doi:10.7326/m14-2385 · Cited by 8,269
Self-citationHow to perform a meta-analysis with R: a practical tutorial
Balduzzi, S., Rücker, G., & Schwarzer, G. (2019) · Evidence Based Mental Health · doi:10.1136/ebmental-2019-300117 · Cited by 5,827
Self-citationSalanti, G., Ades, A. E., & Ioannidis, J. P. A. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.03.016 · Cited by 3,766
Self-citationdplyr: A Grammar of Data Manipulation
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2014) · doi:10.32614/cran.package.dplyr · Cited by 3,603
An algorithm for drawing general undirected graphs
Kamada, T., & Kawai, S. (1989) · Information Processing Letters · doi:10.1016/0020-0190(89)90102-6 · Cited by 2,750
Checking consistency in mixed treatment comparison meta-analysis
Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010) · Statistics in Medicine · doi:10.1002/sim.3767 · Cited by 2,521 · cited 3×
“A method to evaluate local inconsistency in each treatment comparison separately is by using the separate indirect from direct evidence (SIDE) method (Dias, Welton, Caldwell, and Ades 2010).”
Cited by
Sorted by citations
Items 501–532 of 532
Chen, Y., Tai, Y.-H., Huang, P., Chiu, J.-P., & Hung, Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1920738
Wang, C., Huang, Z., Liu, P., Sun, N., Ruan, L., & Qiao, M. (2026) · Open Medicine · doi:10.1515/med-2026-1542
NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R
Noma, H., Maruo, K., Tanaka, S., & Furukawa, T. A. (2026) · Research Synthesis Methods · doi:10.1017/rsm.2026.10122
Furukawa, Y., Siafis, S., Schneider‐Thoma, J., Nomura, N., Fares‐Otero, N. E., Illing, S., Storosum, B. W. C., Zantvoord, J. B., Schoretsanitis, G., Schwarz, C., et al. (2026) · The Lancet Psychiatry · doi:10.1016/s2215-0366(26)00280-4
Self-citationWang, X., Jiao, M., & Ma, W. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71406
Fornaro, M., Lorenzo, C. D., Núñez, N. A., Singh, B., Veldić, M., Sambataro, F., McIntyre, R. S., Frye, M. A., & Miola, A. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122568
Hodrob, T., Saad, R. J., Hamdy, A., Alheneedy, M. S., Elsayed, A. M. S. A., Ghazy, E., & Ayesh, H. (2026) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70353
Moqbel, I., Helal, M. B., Abourageh, A. H., Elhage, J. C., Lashin, A. A., Elazrag, A. M., Ahmed, K. K., Galal, M. N., Altabawi, M., & Capobianco, I. (2026) · Hernia · doi:10.1007/s10029-026-03828-y
Drouri, S., Essanhajy, Z., & Jabri, M. (2026) · Journal of Conservative Dentistry and Endodontics · doi:10.4103/jcde.jcde_776_26
Alsabri, M., Abouelmagd, K., Elsnhory, A. B., Kamal, I., Elsayed, S. M., Shehada, W., Batarseh, S. F., & Gamboa, L. L. (2026) · Current Medical Research and Opinion · doi:10.1080/03007995.2026.2734671
Drug treatments for fibromyalgia: systematic review and network meta-analysis of randomised trials
Zeraatkar, D., Lima, J. P., Kirsh, S., Ling, M., Movahed, H., Li, H., Huynh, A., Jassal, T., Walch, A., Lahiji, M. R., et al. (2026) · BMJ Medicine · doi:10.1136/bmjmed-2026-002902
Fornaro, M., Núñez, N. A., Lorenzo, C. D., Steardo, L., Carvalho, A. F., Singh, B., Veldić, M., Solmi, M., Sambataro, F., Rapaport, M. H., et al. (2026) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2026.113881
Cai, Q., Zeng, Y., Jiménez-Orenga, N., Miguel, C., Harrer, M., Cuijpers, P. J., Díaz‐García, A., & Ye, Q. (2026) · JMIR Research Protocols · doi:10.2196/104791
Wifler, W., Sutherland, D., Frame, D., Gache, L. M., & Gunnarsson, C. (2026) · Journal of Comparative Effectiveness Research · doi:10.57264/cer-2026-0142
Li, Z., 吴树辉, Wu, C., Yin, Y., & Xu, X. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1901781
Singh, P., Sharma, M., Gupta, K., Dhiman, V., Verma, A. K., Malhi, A. S., & Anand, S. (2026) · Expert Review of Anti-infective Therapy · doi:10.1080/14787210.2026.2746348
Alghzawi, F., Alghzawi, Y., Alhassan, L., & Kiyyam, R. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71434
El-Khalifa, B. M., Khelifa, H., Gad, M. M., Ahmad, J., Mazy, H. M., Soliman, A., Nada, E. A., Elfakharany, B., Elmeneizee, T. H., & Qutob, I. A. (2026) · BMC Anesthesiology · doi:10.1186/s12871-026-04328-3
Bischof, K., Moitzi, A. M., Πετροπούλου, Μ., Stubbs, B., & König, D. (2026) · Journal of the International Society of Sports Nutrition · doi:10.1080/15502783.2026.2740224
Hayek, M. A., Sawaf, B., & Elhadi, M. (2026) · International Journal of Colorectal Disease · doi:10.1007/s00384-026-05253-0
Network Meta‐Analysis: Treatments for Acute Alcohol‐Associated Hepatitis
Gallaher, C., Aithal, G. P., Dhanda, A., Forrest, E. H., O’Beirne, J. P., Liu, K., Thursz, M. R., & Parker, R. A. (2026) · Liver International · doi:10.1111/liv.70922
Dinges, H., Thiedmann, S., Weyland, J., Volberg, C., Schmitt, F. C. F., Wulf, H., & Schubert, A. (2026) · Pain · doi:10.1097/j.pain.0000000000004132
A Path‐Based Approach for Detecting and Assessing Inconsistency in Network Meta‐Analysis
Tahmasebi, N. R., Davies, A. L., Papakonstantinou, T., Rücker, G., & Nikolakopoulou, A. (2026) · Statistics in Medicine · doi:10.1002/sim.70752
Self-citationChen, H., Wang, K., Jiang, Q., Zeng, D., Yang, J., Wang, X., Huang, L., & Pu, G. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.129328
Yang, Q., Li, X., & Zhou, M. (2026) · Brain Sciences · doi:10.3390/brainsci16101128
Gill, O. A., Shawwa, A., Alhamwi, N., Saleh, A. O., Gill, M. U., Rahim, M. W., Chaudary, M., & Lapenda, I. (2026) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-026-06028-8
Le, H. T., Apriadi, D., Tu, Y., Chiu, H., & Huang, H. (2026) · Journal of Advanced Nursing · doi:10.1111/jan.70788
Tavakoli, K., Ansari, A., Alaei, E., Jahanfard, R., Mansouri, E. S., Ganjalikhani, M., Selk-Ghaffari, M., Shahi, M. H. P., & Memari, A. H. (2026) · Journal of Cardiology · doi:10.1016/j.jjcc.2026.10.004
Kabnick, L. S., Esponda, O. L., Jimenez, J. C., Vargas, L. M., Gache, L. M., Frame, D., Gunnarsson, C., & Ozsvath, K. J. (2026) · Journal of Vascular Surgery Venous and Lymphatic Disorders · doi:10.1016/j.jvsv.2026.102648
Liu, Z., Wang, Y., Zhang, M., Sanchez, M., Fu, Y., Sun, H., & Luying, S. (2026) · medRxiv · doi:10.64898/2026.10.25.26366409
Kim, J., & Chung, T. N. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15218364
Gnanavelou, M., Ho, G., Ng, C. H., Tioh, J. K., Tham, E. K. J., Chan, T. W., Yeo, L. Y. Y., Sasikumar, N. A., Yang, H. S., Lee, W. X., et al. (2026) · EClinicalMedicine · doi:10.1016/j.eclinm.2026.104300
Metadata
- Authors
- Balduzzi, S., Rücker, G., Nikolakopoulou, A., Papakonstantinou, T., Salanti, G., Efthimiou, O., & Schwarzer, G.
- Affiliations
- University of Freiburg (DE)
- University of Bern (CH)
- ORCID
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2023
- Volume
- 106
- Issue
- 2
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W4327640222
- Cited by
- 532
- References
- 67
- Citations by year
- 2026: 221 · 2025: 176 · 2024: 108 · 2023: 27
- FWCI
- 98.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Statistical Software (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation
- Deutsche Forschungsgemeinschaft
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (186723, 179158, 180083)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
- Keywords
- network meta-analysis, frequentist methods, direct and indirect evidence, intervention comparison, statistical software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review