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Meta-Analysis and Subgroups
Abstract
Subgroup analysis is the process of comparing a treatment effect for two or more variants of an intervention-to ask, for example, if an intervention's impact is affected by the setting (school versus community), by the delivery agent (outside facilitator versus regular classroom teacher), by the quality of delivery, or if the long-term effect differs from the short-term effect. While large-scale studies often employ subgroup analyses, these analyses cannot generally be performed for small-scale studies, since these typically include a homogeneous population and only one variant of the intervention. This limitation can be bypassed by using meta-analysis. Meta-analysis allows the researcher to compare the treatment effect in different subgroups, even if these subgroups appear in separate studies. We discuss several statistical issues related to this procedure, including the selection of a statistical model and statistical power for the comparison. To illustrate these points, we use the example of a meta-analysis of obesity prevention.
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“Because the results from our subgroup analyses are purely observational (Borenstein & Higgins, 2013), we cannot conclude that treatment type and format caused the differences in outcomes we observed in outcomes.”
From Systematic review and meta-analysis of transdiagnostic psychological treatments for anxiety and depressive disorders in adulthood (2015) · cites this paper as Borenstein & Higgins, 2013
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Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075823 · Cited by 18,773
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
Self-citationWasserman, S. S., Hedges, L. V., & Olkin, I. (1988) · Journal of Educational Statistics · doi:10.2307/1164953 · Cited by 10,377
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799
Self-citation(2000) · Cited by 9,198
A basic introduction to fixed-effect and random-effects models for meta-analysis
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010) · Research Synthesis Methods · doi:10.1002/jrsm.12 · Cited by 6,793
Self-citationStatistical Methods for Meta-Analysis.
Freeman, P. R., Hedges, L. V., & Olkin, I. (1986) · Biometrics · doi:10.2307/2531069 · Cited by 4,840
The Handbook of Research Synthesis and Meta-Analysis
(2019) · doi:10.7758/9781610448864 · Cited by 4,553
The power of statistical tests in meta-analysis
Hedges, L. V., & Pigott, T. D. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.3.203 · Cited by 716 · cited 1×
“In their papers on statistical power for comparing subgroups, Hedges and Pigott (2001, 2004) note that some researchers believe that when we use metaanalysis to compare the effect across subgroups, statistical power is uniformly high.”
BackgroundThe power of statistical tests for moderators in meta-analysis
Hedges, L. V., & Pigott, T. D. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.426 · Cited by 672
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Markov, A., Hauser, L., & Chaabene, H. (2023) · Sports Medicine · doi:10.1007/s40279-022-01764-2 · Cited by 66
Occupational exposure to formaldehyde and risk of lung cancer: A systematic review and meta‐analysis
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Conde‐Agudelo, A., & Romero, R. J. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.04.023 · Cited by 66
Tenets for the Proper Conduct and Use of Meta-Analyses: A Practical Guide for Neurosurgeons
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Self-citationQUIGLEY, M. A., Embleton, N. D., Meader, N., & McGuire, W. L. (2024) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd002971.pub6 · Cited by 63
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Lee, D., Chiavaroli, L., Ayoub‐Charette, S., Khan, T. A., Zurbau, A., Au‐Yeung, F., Cheung, A., Liu, Q., Qi, X., Ahmed, A., et al. (2022) · Nutrients · doi:10.3390/nu14142846 · Cited by 72
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Gualano, M. R., Corradi, A., Voglino, G., Catozzi, D., Olivero, E., Corezzi, M., Bert, F., & Siliquini, R. (2021) · Vaccine · doi:10.1016/j.vaccine.2020.12.061 · Cited by 61
Acosta‐Manzano, P., Rodriguez‐Ayllon, M., Acosta, F. M., Niederseer, D., & Niebauer, J. (2020) · Obesity Reviews · doi:10.1111/obr.13007 · Cited by 59
Gharakhanlou, R., Wesselmann, L., Rademacher, A., Lampit, A., Negaresh, R., Kaviani, M., Oberste, M., Motl, R. W., Sandroff, B. M., Bansi, J., et al. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520917935 · Cited by 64
Rubio‐Aparicio, M., Sánchez‐Meca, J., López‐López, J. A., Botella, J., & Marín‐Martínez, F. (2017) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12092 · Cited by 56
Batdı, V., Doğan, Y., & Talan, T. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.1954035 · Cited by 70
Guo, J., Chen, X., Guo, Y., Liu, M., Li, P., Tao, Y., Liu, Z., Yang, Z., Zhan, S., & Sun, F. (2024) · Vaccine · doi:10.1016/j.vaccine.2024.02.059 · Cited by 61
Kang, N., Summers, J. J., & Cauraugh, J. H. (2016) · Brain stimulation · doi:10.1016/j.brs.2016.05.005 · Cited by 59
van Diemen, T., Crul, T., van Nes, I., Geertzen, J. H., & Post, M. W. (2017) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2017.03.024 · Cited by 59
Drought has negative consequences on aphid fitness and plant vigor: Insights from a meta‐analysis
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Jung, M., Zou, L., Yu, J. J., Ryu, S., Kong, Z., Yang, L., Kang, M., Lin, J., Li, H., Smith, L., et al. (2020) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2020.04.004 · Cited by 77
Consumer willingness to pay for vehicle attributes: What do we Know?
Greene, D. L., Hossain, A., Hofmann, J., Helfand, G. E., & Beach, R. (2018) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2018.09.013 · Cited by 56
Mineralocorticoid receptor antagonists and atrial fibrillation: a meta-analysis of clinical trials
Oraii, A., Healey, J. S., Kowalik, K., Pandey, A. K., Benz, A. P., Wong, J. A., Conen, D., & McIntyre, W. F. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehad811 · Cited by 57
Kosztin, A., Vámos, M., Aradi, D., Schwertner, W. R., Kovács, A. D., Nagy, K. V., Zima, E., Gellér, L., Duray, G. Z., Kutyifa, V., et al. (2017) · Heart Failure Reviews · doi:10.1007/s10741-017-9652-1 · Cited by 55
Beaussart, M.-L., Barbarot, S., Mauger, C., & Roy, A. (2018) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617718000383 · Cited by 55
Binge-Watching and Mental Health Problems: A Systematic Review and Meta-Analysis
Alimoradi, Z., Jafari, E., Potenza, M. N., Lin, C., Wu, C.-Y., & Pakpour, A. H. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159707 · Cited by 54
Abdelgawwad, M. S., Alston, E. A., & Eltoum, I. A. (2013) · Cancer Cytopathology · doi:10.1002/cncy.21337 · Cited by 54
Treating treatment non-responders: A meta-analysis of randomized controlled psychotherapy trials
Gloster, A. T., Rinner, M. T. B., Ioannou, M., Villanueva, J., Block, V. J., Ferrari, G., Benoy, C., Bader, K., & Karekla, M. (2020) · Clinical Psychology Review · doi:10.1016/j.cpr.2019.101810 · Cited by 53
Zhang, B., Kan, R. L. D., Dong, A., Zhang, J., Bai, Z., Xie, Y., Liu, Q., & Peng, Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221166 · Cited by 53
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Oleszkiewicz, S., & Watson, S. J. (2020) · Applied Cognitive Psychology · doi:10.1002/acp.3767 · Cited by 58
Karimian, J., Hadi, A., Pourmasoumi, M., Najafgholizadeh, A., & Ghavami, A. (2019) · Phytotherapy Research · doi:10.1002/ptr.6356 · Cited by 52
Kazakos, C. T., & Karageorgiou, V. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz106 · Cited by 53
Hadi, A., Pourmasoumi, M., Najafgholizadeh, A., Clark, C. C. T., & Esmaillzadeh, A. (2021) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-021-03351-w · Cited by 59
Kovalic, A. J., Huang, G., Thuluvath, P. J., & Satapathy, S. K. (2020) · Hepatology · doi:10.1002/hep.31472 · Cited by 51
Zhang, Y., Duy, S. V., Munoz, G., & Sauvé, S. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152104 · Cited by 50
Tan, J., Cheng, L. J., Chan, E., Lau, Y., & Lau, S. T. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.01.013 · Cited by 52
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Cicero, A. F. G., Pirro, M., Watts, G. F., Mikhailidis, D. P., Banach, M., & Sahebkar, A. (2017) · Drugs · doi:10.1007/s40265-017-0839-5 · Cited by 52
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Supplee, L. H., Kelly, B. C., MacKinnon, D. P., & Barofsky, M. Y. (2012) · Prevention Science · doi:10.1007/s11121-012-0335-9 · Cited by 54
Efendi, D., Apriliyasari, R. W., Massie, J. G. E. P., Wong, C. L., Natalia, R., Utomo, B., Edison, C., Apriyanti, E., & Chen, K. (2023) · BMC Nursing · doi:10.1186/s12912-023-01312-x · Cited by 64
Pourmasoumi, M., Hadi, A., Najafgholizadeh, A., Joukar, F., & Mansour‐Ghanaei, F. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2019.04.003 · Cited by 51
Kang, N., Lee, R. D., Lee, J. H., & Hwang, M. (2019) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2019.09.003 · Cited by 49
Horsley, B. J., Tofari, P. J., Halson, S. L., Kemp, J. G., Dickson, J., Maniar, N., & Cormack, S. J. (2021) · Sports Medicine · doi:10.1007/s40279-021-01443-8 · Cited by 49
Wilson, E. J., Abbott, M. J., & Norton, A. R. (2023) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2023.102729 · Cited by 52
Efficacy of hypnosis in adults undergoing surgical procedures: a meta-analytic update
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Location‐scale models for meta‐analysis
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Zito, A., Princi, G., Romiti, G. F., Galli, M., Basili, S., Liuzzo, G., Sanna, T., Restivo, A., Ciliberti, G., Trani, C., et al. (2022) · European Journal of Heart Failure · doi:10.1002/ejhf.2655 · Cited by 48
Conde‐Agudelo, A., Romero, R. J., Rehal, A., Brizot, M. D. L., Serra, V., da Fonseca, E. B., Çetingöz, E., Syngelaki, A., Alfredo Perales, M., Hassan, S. S., et al. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.05.010 · Cited by 55
Meta‐analysis: Exercise intervention for sleep problems in cancer patients
Fang, Y., Hung, C., Chan, J., Huang, S., & Lee, Y. (2019) · European Journal of Cancer Care · doi:10.1111/ecc.13131 · Cited by 53
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de Winter, L., Couwenbergh, C., van Weeghel, J., Hasson-Ohayon, I., Vermeulen, J. M., Mulder, C. L., Boonstra, N., Klaver, K. M., Oud, M., de Haan, L., et al. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2021.11.010 · Cited by 46
Wuerdemann, N., Jain, R. A., Adams, A., Speel, E. M., Wagner, S., Joosse, S. A., & Klußmann, J. P. (2020) · Cancers · doi:10.3390/cancers12102997 · Cited by 48
Wang, C., Zhang, C., Li, S., Yu, L., Tian, F., Zhao, J., Zhang, H., Chen, W., & Zhai, Q. (2020) · Foods · doi:10.3390/foods9111540 · Cited by 48
Schollaert, P., Crott, R., Bertrand, C., D'Hondt, L. A., Borght, T. D. V., & Krug, B. (2014) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-014-2488-2 · Cited by 46
Barhoun, P., Fuelscher, I., Kothe, E., He, J. L., Youssef, G., Enticott, P., Williams, J., & Hyde, C. (2019) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.002 · Cited by 48
Tinner, L., Caldwell, D. M., Hickman, M., MacArthur, G. J., Gottfredson, D. C., Lana, A., Moberg, D. P., Wolfe, D. A., & Campbell, R. M. (2018) · BMC Public Health · doi:10.1186/s12889-018-6042-0 · Cited by 45
Abd-Alrazaq, A. A., Alhuwail, D., Al-Jafar, E., Ahmed, A., Shuweihdi, F., Reagu, S. M., & Househ, M. S. (2022) · JMIR Serious Games · doi:10.2196/35202 · Cited by 56
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Westwood, S. J., Aggensteiner, P.-M., Kaiser, A., Nagy, P., Donno, F., Merkl, D., Balia, C., Goujon, A., Bousquet, E., Capodiferro, A. M., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2024.3702 · Cited by 56
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Melnyk, S. A., Flynn, B. B., & Awaysheh, A. (2017) · International Journal of Production Research · doi:10.1080/00207543.2017.1391423 · Cited by 44
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Reis, D. J., Casteen, E. J., & Ilardi, S. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36507-9 · Cited by 45
Rumpf, C., Proschinger, S., Schenk, A., Bloch, W., Lampit, A., Javelle, F., & Zimmer, P. (2021) · Sports Medicine · doi:10.1007/s40279-020-01402-9 · Cited by 45
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Evrenoglou, T., Metelli, S., & Chaimani, A. (2022) · Springer eBooks · doi:10.1007/978-3-319-52636-2_287 · Cited by 53
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Kazemian, E., Pourali, A., Sedaghat, F., Karimi, M., Basirat, V., Hezaveh, Z. S., Davoodi, S. H., & Holick, M. F. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac068 · Cited by 42
Quinn, E. A., Millard, E., & Jones, J. M. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00368-1 · Cited by 52
Wu, Y., Zhu, W., Sun, C.-Q., Yue, X., Zheng, M., Fu, G., & Gong, X. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1002342 · Cited by 42
López‐Gil, J. F., López, P. J. T., Hershey, M. S., López‐Bueno, R., Gutiérrez‐Espinoza, H., Soler, A., Fernández‐Montero, A., & Victoria‐Montesinos, D. (2023) · Journal of Global Health · doi:10.7189/jogh.13.04087 · Cited by 46
Sahebkar, A., Șerban, M.-C., Penson, P. E., Gurban, C., Ursoniu, S., Toth, P. P., Jones, S. R., Lippi, G., Kotani, K., Kostner, K., et al. (2017) · Drugs · doi:10.1007/s40265-017-0767-4 · Cited by 41
Tang, Y., & He, W. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1018558 · Cited by 70
Kavanagh, M., Brett, C., & Brignell, C. M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107776 · Cited by 49
Chandeying, N., & Thongseiratch, T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00912-w · Cited by 40
Perezgrovas‐Olaria, R., Alzghari, T., Rahouma, M., Dimagli, A., Harik, L., Soletti, G. J., An, K. R., Caldonazo, T. M. M., Kirov, H., Cancelli, G., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.123.030907 · Cited by 41
Domingo, R. A., Tripathi, S., Perez‐Vega, C., Vivas-Buitrago, T. G. M., Lu, V. M., Todnem, N. D., Quiñones‐Hinojosa, A., & Tawk, R. G. (2020) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2020-016294 · Cited by 40
Zhang, Z., Zhou, Y., Lin, S., Xiao, J., Ai, W., & Zhang, W. W. (2021) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2021.10.050 · Cited by 40
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Menardi, A., Dotti, L., Ambrosini, E., & Vallesi, A. (2022) · Journal of Neurology · doi:10.1007/s00415-022-11236-2 · Cited by 42
Kerzhner, O., Berla, E., Har‐Even, M., Ratmansky, M., & Goor‐Aryeh, I. (2023) · Pain Practice · doi:10.1111/papr.13277 · Cited by 44
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Bell, S. H., Olsen, R. B., Orr, L. L., & Stuart, E. A. (2016) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373715617549 · Cited by 39
Bioletto, F., Barale, M., Parasiliti‐Caprino, M., Prencipe, N., Berton, A. M., Massimo, P., Deandreis, D., & Ghigo, E. (2021) · European Journal of Endocrinology · doi:10.1530/eje-21-0038 · Cited by 39
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Thomson, S., Ng, N. Y., Riehle, M. O., Kingham, P. J., Dahlin, L. B., Wiberg, M., & Hart, A. M. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd012574.pub2 · Cited by 39
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YİĞİTER, M. S., Demir, S., & Doğan, N. (2023) · Current Psychology · doi:10.1007/s12144-023-04972-9 · Cited by 51
Bo, A., Mao, W., & Lindsey, M. A. (2017) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4688 · Cited by 38
A Meta-Analysis Examining the Role of Character-Recipient Similarity in Narrative Persuasion
Chen, M., Dong, Y., & Wang, J. (2023) · Communication Research · doi:10.1177/00936502231204834 · Cited by 46
Teaching higher order thinking skills to gifted students: A meta-analysis
Lo, C. O., & Feng, L. (2020) · Gifted Education International · doi:10.1177/0261429420917854 · Cited by 35
Allison, B. A., Widman, L. M., Stewart, J., Evans-Paulson, R., & Perry, M. F. (2021) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2021.04.001 · Cited by 37
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de Oliveira, M. M., Pereira, C. R., de Oliveira, I. R. C., Godfroid, J., Lage, A. P., & Dorneles, E. M. S. (2021) · Transboundary and Emerging Diseases · doi:10.1111/tbed.14259 · Cited by 37
Cheng, C. K. T., Chua, J. H., Cheng, L. J., Ang, W. H. D., & Lau, Y. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13558 · Cited by 42
Edessa, D., Assefa, N., Dessie, Y., Kumsa, F. A., Dinsa, G. D., & Oljira, L. (2022) · Journal of Pharmaceutical Policy and Practice · doi:10.1186/s40545-022-00454-8 · Cited by 37
Lee, C. K., Friedlander, M. L., Tjokrowidjaja, A., Ledermann, J. A., Coleman, R. L., Mirza, M. R., Matulonis, U. A., Pujade‐Lauraine, E., Bloomfield, R., Goble, S., et al. (2021) · Cancer · doi:10.1002/cncr.33517 · Cited by 36
Chiavaroli, L., Cheung, A., Ayoub‐Charette, S., Ahmed, A., Lee, D., Au‐Yeung, F., Qi, X., Back, S., McGlynn, N. D., Ha, V., et al. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2023.01.023 · Cited by 39
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Norhayati, M. N., Yusof, R. C., & Azman, M. Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18179157 · Cited by 38
Lepard, J. R., Spaggiari, R., Corley, J. A., Barthélemy, E. J., Kim, E. E., Patterson, R. H., Venturini, S., Still, M. E., Lo, Y. T., Rosseau, G. L., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003795 · Cited by 36
Zarezadeh, M., Mahmoudinezhad, M., Faghfouri, A. H., Honarvar, N. M., Regestein, Q. R., Papatheodorou, S., Mekary, R. A., & Willett, W. C. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102419 · Cited by 42
Zannou, O. M., Ouedraogo, A. S., Biguezoton, A. S., Abatih, E. N., Coral-Almeida, M., Farougou, S., Yao, K. P., Lempereur, L., & Saegerman, C. (2021) · Pathogens · doi:10.3390/pathogens10070893 · Cited by 35
Tsivgoulis, G. K., Palaiodimou, L., Triantafyllou, S., Köhrmann, M., Dilaveris, P., Tsioufis, K. P., Magiorkinis, G., Krogias, C., Schellinger, P. D., Caso, V., et al. (2022) · European Stroke Journal · doi:10.1177/23969873221139410 · Cited by 35
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Metadata
- Authors
- Borenstein, M., & Higgins, J. P. T.
- Affiliations
- Biostat (United States) (US)
- University of Bristol (GB)
- Corresponding author
- Borenstein, Michael
- Venue
- Prevention Science
- Published
- 2013-03-13
- Volume
- 14
- Issue
- 2
- Pages
- 134-143
- ISSN
- 1389-4986 (ISSN-L)
- 1573-6695
- OpenAlex
- W2032737645
- Cited by
- 767
- References
- 17
- Citations by year
- 2026: 68 · 2025: 95 · 2024: 134 · 2023: 124 · 2022: 94 · 2021: 86 · 2020: 45 · 2019: 31 · 2018: 19 · 2017: 29 · 2016: 14 · 2015: 12 · 2014: 8 · 2013: 3
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Prevention Science (publishedVersion)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- National Institutes of Health (5r44da019280-03, 5r44da022799-03, 4r44da025366-02)
- Medical Research Council
- National Institute on Drug Abuse (DA022799, DA019280, DA025366)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- subgroup analysis, meta-analysis, treatment effect heterogeneity, statistical power, statistical models, obesity prevention, delivery agents
- MeSH terms
- Data Interpretation, Statistical, Humans, Models, Theoretical, Obesity, Morbid
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