The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data
Open accessAbstract
OBJECTIVES: Estimation of between-study heterogeneity is problematic in small meta-analyses. Bayesian meta-analysis is beneficial because it allows incorporation of external evidence on heterogeneity. To facilitate this, we provide empirical evidence on the likely heterogeneity between studies in meta-analyses relating to specific research settings. STUDY DESIGN AND SETTING: Our analyses included 6,492 continuous-outcome meta-analyses within the Cochrane Database of Systematic Reviews. We investigated the influence of meta-analysis settings on heterogeneity by modeling study data from all meta-analyses on the standardized mean difference scale. Meta-analysis setting was described according to outcome type, intervention comparison type, and medical area. Predictive distributions for between-study variance expected in future meta-analyses were obtained, which can be used directly as informative priors. RESULTS: Among outcome types, heterogeneity was found to be lowest in meta-analyses of obstetric outcomes. Among intervention comparison types, heterogeneity was lowest in meta-analyses comparing two pharmacologic interventions. Predictive distributions are reported for different settings. In two example meta-analyses, incorporating external evidence led to a more precise heterogeneity estimate. CONCLUSION: Heterogeneity was influenced by meta-analysis characteristics. Informative priors for between-study variance were derived for each specific setting. Our analyses thus assist the incorporation of realistic prior information into meta-analyses including few studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Self-citationGeneral methods for monitoring convergence of iterative simulations
Brooks, S. P., & Gelman, A. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474787 · Cited by 6,045
A Re-Evaluation of Random-Effects Meta-Analysis
Higgins, J. P. T., Thompson, S. G., & Spiegelhalter, D. J. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00552.x · Cited by 2,569 · cited 2×
“Alternatively, we can allow for heterogeneity in a random-effects metaanalysis, estimating the summary effect and the betweenstudy variance [2].”
BackgroundSelf-citationTurner, R. M., Davey, J., Clarke, M. J., Thompson, S. G., & Higgins, J. P. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys041 · Cited by 837
Self-citationMeta‐Analysis Of Controlled Clinical Trials
Whitehead, A. (2002) · doi:10.1002/0470854200 · Cited by 827
Lambert, P. C., Sutton, A. J., Burton, P. R., Abrams, K. R., & Jones, D. R. (2005) · Statistics in Medicine · doi:10.1002/sim.2112 · Cited by 511
Davey, J., Turner, R. M., Clarke, M. J., & Higgins, J. P. (2011) · BMC Medical Research Methodology · doi:10.1186/1471-2288-11-160 · Cited by 404
Self-citation
Research works printed but not linked: 12
- Sutton, 2009, Evidence synthesis as the key to more coherent and efficient research, BMC Med Res Methodol, 9, 29, 10.1186/1471-2288-9-29 · DOI
- Higgins, 1996, Borrowing strength from external trials in a meta-analysis, Stat Med, 15, 2733, 10.1002/(SICI)1097-0258(19961230)15:24<2733::AID-SIM562>3.0.CO;2-0 · DOI
- Pullenayegum, 2011, An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes, Stat Med, 30, 3082, 10.1002/sim.4326 · DOI
- Spiegelhalter, 2002, Bayesian measures of model complexity and fit (with discussion), J R Stat Soc Ser B, 64, 583, 10.1111/1467-9868.00353 · DOI · Paper page
- Lunn, 2000, WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility, Stat Comput, 10, 325, 10.1023/A:1008929526011 · DOI
- Heiwe, 2011, Exercise training for adults with chronic kidney disease, Cochrane Database Syst Rev, CD003236
- Viechtbauer, 2007, Confidence intervals for the amount of heterogeneity in meta-analysis, Stat Med, 26, 37, 10.1002/sim.2514 · DOI
- Thompson, 1997, Investigating underlying risk as a source of heterogeneity in meta-analysis, Stat Med, 16, 2741, 10.1002/(SICI)1097-0258(19971215)16:23<2741::AID-SIM703>3.0.CO;2-0 · DOI
- Adams, 2000, Budesonide at different doses for chronic asthma, Cochrane Database Syst Rev, CD003271
- Anzures-Cabrera, 2011, Expressing findings from meta-analyses of continuous outcomes in terms of risks, Stat Med, 30, 2967, 10.1002/sim.4298 · DOI
- Wu, 2005, Chinese medical herbs for chemotherapy side effects in colorectal cancer patients, Cochrane Database Syst Rev, CD004540
- Friedrich, 2008, The ratio of means method as an alternative to mean differences for analyzing continuous outcome variables in meta-analysis: a simulation study, BMC Med Res Methodol, 8, 32, 10.1186/1471-2288-8-32 · DOI · Paper page
Cited by
Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(17)32802-7 · Cited by 3,541
Self-citationCINeMA: an approach for assessing confidence in the results of a network meta-analysis
Nikolakopoulou, A., Higgins, J. P. T., Papakonstantinou, T., Chaimani, A., Del Giovane, C., Egger, M., & Salanti, G. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003082 · Cited by 1,935
Self-citationHuhn, M., Nikolakopoulou, A., Schneider-Thoma, J., Krause, M., Samara, M., Peter, N., Arndt, T., Bäckers, L., Rothe, P., Cipriani, A., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31135-3 · Cited by 1,753
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis
Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G., Kuss, O., Higgins, J. P., Langan, D., & Salanti, G. (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1164 · Cited by 1,685
Self-citationCortese, S., Adamo, N., Del Giovane, C., Mohr-Jensen, C., Hayes, A. J., Carucci, S., Atkinson, L. Z., Tessari, L., Banaschewski, T., Coghill, D., et al. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30269-4 · Cited by 1,402
Karyotaki, E., Efthimiou, O., Miguel, C., Maas genannt Bermpohl, F., Furukawa, T. A., Cuijpers, P., Individual Patient Data Meta-Analyses for Depression (IPDMA-DE) Collaboration, Riper, H., Patel, V., Mira, A., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.4364 · Cited by 866
Papakonstantinou, T., Nikolakopoulou, A., Higgins, J. P. T., Egger, M., & Salanti, G. (2020) · Campbell Systematic Reviews · doi:10.1002/cl2.1080 · Cited by 596
Self-citationGe, L., Sadeghirad, B., Ball, G. D. C., da Costa, B. R., Hitchcock, C. L., Svendrovski, A., Kiflen, R., Quadri, K., Kwon, H. Y., Karamouzian, M., et al. (2020) · BMJ · doi:10.1136/bmj.m696 · Cited by 426
Cuijpers, P., Quero, S., Noma, H., Ciharova, M., Miguel, C., Karyotaki, E., Cipriani, A., Cristea, I. A., & Furukawa, T. A. (2021) · World Psychiatry · doi:10.1002/wps.20860 · Cited by 388
Tsapas, A., Avgerinos, I., Karagiannis, T., Malandris, K., Manolopoulos, A., Andreadis, P., Liakos, A., Matthews, D. R., & Bekiari, E. (2020) · Annals of Internal Medicine · doi:10.7326/m20-0864 · Cited by 346
Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., et al. (2018) · Focus · doi:10.1176/appi.focus.16407 · Cited by 328
Self-citationBayesian Random-Effects Meta-Analysis Using the bayesmeta R Package
Röver, C. (2020) · Journal of Statistical Software · doi:10.18637/jss.v093.i06 · Cited by 310
Methods for evidence synthesis in the case of very few studies
Bender, R., Friede, T., Koch, A., Kuss, O., Schlattmann, P., Schwarzer, G., & Skipka, G. (2018) · Research Synthesis Methods · doi:10.1002/jrsm.1297 · Cited by 250
Veroniki, A. A., Jackson, D., Bender, R., Kuss, O., Langan, D., Higgins, J. P. T., Knapp, G., & Salanti, G. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1319 · Cited by 236
Self-citationFurukawa, T. A., Salanti, G., Atkinson, L. Z., Leucht, S., Ruhe, H. G., Turner, E. H., Chaimani, A., Ogawa, Y., Takeshima, N., Hayasaka, Y., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010919 · Cited by 214
Bighelli, I., Salanti, G., Huhn, M., Schneider‐Thoma, J., Krause, M., Reitmeir, C., Wallis, S., Schwermann, F., Pitschel‐Walz, G., Barbui, C., et al. (2018) · World Psychiatry · doi:10.1002/wps.20577 · Cited by 199
Tsapas, A., Karagiannis, T., Kakotrichi, P., Avgerinos, I., Mantsiou, C., Tousinas, G., Manolopoulos, A., Liakos, A., Malandris, K., Matthews, D. R., et al. (2021) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.14451 · Cited by 179
Mavranezouli, I., Megnin‐Viggars, O., Daly, C., Dias, S., Stockton, S., Meiser‐Stedman, R., Trickey, D., & Pilling, S. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13094 · Cited by 172
Watt, J. A., Goodarzi, Z., Veroniki, A. A., Nincic, V., Khan, P. A., Ghassemi, M., Thompson, Y., Tricco, A. C., & Straus, S. E. (2019) · Annals of Internal Medicine · doi:10.7326/m19-0993 · Cited by 159
Watt, J. A., Goodarzi, Z., Veroniki, A. A., Nincic, V., Khan, P. A., Ghassemi, M., Lai, Y., Treister, V., Thompson, Y., Schneider, R., et al. (2021) · BMJ · doi:10.1136/bmj.n532 · Cited by 143
Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases
Friede, T., Röver, C., Wandel, S., & Neuenschwander, B. (2017) · Biometrical Journal · doi:10.1002/bimj.201500236 · Cited by 128
Beyond Randomized Clinical Trials: Use of External Controls
Schmidli, H., Häring, D. A., Thomas, M., Cassidy, A., Weber, S., & Bretz, F. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1723 · Cited by 125
Bayesian Meta-Analysis with Weakly Informative Prior Distributions
Williams, D. R., Rast, P., & Bürkner, P. C. (2018) · doi:10.31234/osf.io/7tbrm · Cited by 124
Brief Behavioral Interventions for Substance Use in Adolescents: A Meta-analysis
Steele, D. W., Becker, S. J., Danko, K. J., Balk, E. M., Adam, G. P., Saldanha, I. J., & Trikalinos, T. A. (2020) · Pediatrics · doi:10.1542/peds.2020-0351 · Cited by 99
Yildiz, A., Siafis, S., Mavridis, D., Vieta, E., & Leucht, S. (2023) · The Lancet Psychiatry · doi:10.1016/s2215-0366(23)00199-2 · Cited by 104
Zhou, X., Zhang, Y., Furukawa, T. A., Cuijpers, P., Pu, J., Weisz, J. R., Yang, L., Hetrick, S. E., Del Giovane, C., Cohen, D., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3070 · Cited by 80
Kadakia, A., Dembek, C., Heller, V., Singh, R., Uyei, J., Hagi, K., Nosaka, T., & Loebel, A. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03220-3 · Cited by 79
Michopoulos, I., Furukawa, T. A., Noma, H., Kishimoto, S., Onishi, A., Ostinelli, E. G., Ciharova, M., Miguel, C., Karyotaki, E., & Cuijpers, P. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.12.012 · Cited by 69
Zhu, Y., Zhang, C., Siafis, S., Zhuo, K., Zhu, D., Wu, H., Liu, D., Jiang, K., Wang, J., Leucht, S., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.08.013 · Cited by 69
Dong, S., Schneider-Thoma, J., Bighelli, I., Siafis, S., Wang, D., Burschinski, A., Schestag, K., Samara, M., & Leucht, S. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-023-01654-2 · Cited by 68
Boutron, I., Chaimani, A., Devane, D., Meerpohl, J. J., Rada, G., Hróbjartsson, A., Tovey, D., Grasselli, G., & Ravaud, P. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013769 · Cited by 41
Saueressig, T., Pedder, H., Bowe, S. J., Owen, P. J., & Belavy, D. L. (2021) · Journal of Orthopaedic & Sports Physical Therapy · doi:10.2519/jospt.2021.0107 · Cited by 41
Interventions for Substance Use Disorders in Adolescents: A Systematic Review
Steele, D. W., Becker, S. J., Danko, K. J., Balk, E. M., Saldanha, I. J., Adam, G. P., Bagley, S. M., Friedman, C., Spirito, A., Scott, K., et al. (2020) · doi:10.23970/ahrqepccer225 · Cited by 34
Shinohara, K., Efthimiou, O., Ostinelli, E. G., Tomlinson, A., Geddes, J. R., Nierenberg, A. A., Ruhe, H. G., Furukawa, T. A., & Cipriani, A. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027574 · Cited by 27
Iannone, A., Natale, P., Palmer, S. C., Nicolucci, A., Rendina, M., Giorgino, F., Laviola, L., Di Leo, A., & Strippoli, G. F. M. (2023) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.15138 · Cited by 27
Singer, A., Wolf, M., Generoso, L., Arias, E., Delcastillo, K., Echevarria, E., Martinez, A., Androulakis Korakakis, P., Refalo, M. C., Swinton, P. A., et al. (2024) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2024.1429789 · Cited by 14
“For the between-studies standard deviation, informative priors were based on an analysis of the predictive distributions generated from a large number of previous meta-analyses (26).”
Method
Metadata
- Authors
- Rhodes, K. M., Turner, R. M., & Higgins, J. P. T.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 68
- Issue
- 1
- Pages
- 52-60
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2075046475
- Cited by
- 395
- References
- 7
- FWCI
- 17.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- creativecommons.org/licenses/by/3.0/ (vor)
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
