Extreme Heterogeneity in Global Prevalence Meta-Analyses: Evaluating Current Practices and Exploring Bayesian Alternatives - an Umbrella Review
DOI:
https://doi.org/10.6000/1929-6029.2026.15.01Keywords:
Meta-Analysis, Prevalence, Epidemiologic Methods, Biostatistics, Systematic Reviews, Heterogeneity, Publication Bias, Research Design, Evidence-Based Medicine, Public HealthAbstract
Introduction: Global prevalence meta-analyses often exhibit extreme heterogeneity (I² > 90%), yet criteria designed for clinical trials, where homogeneity is desirable, continue to be applied without recognizing that in prevalence studies, variability reflects real differences between populations.
Objective: To document the magnitude of heterogeneity in global prevalence meta-analyses, evaluate the methodological strategies employed for its exploration and management, and explore through illustrative application how Bayesian methods—rarely employed in prevalence meta-analyses—compare with standard frequentist approaches.
Methods: Umbrella review conducted according to PRIOR guidelines. Systematic search in SCOPUS for systematic reviews with global/worldwide prevalence meta-analyses published between 2015-2025. Data were extracted on I², statistical models, subgroup analyses, sensitivity analyses, meta-regression, and prediction intervals. Three meta-analyses were randomly selected for illustrative Bayesian re-analysis using hierarchical models with weakly informative priors, and the results were compared with those from frequentist approaches.
Results: Of 53 included meta-analyses, 52 (98.1%) presented I²≥75%, 47 (88.7%) I²≥90%, and 34 (64.2%) I²>99%. Management strategies showed a decreasing implementation rate: subgroup analyses (96.2%), sensitivity analyses (64.2%), meta-regression (34.0%), and prediction intervals (5.8%). Among studies with I²≥75%, 63.5% provided explicit justification for proceeding with pooling. The illustrative Bayesian analysis of three randomly selected studies demonstrated excellent concordance with frequentist estimates (differences <0.1%), while providing additional uncertainty quantification for heterogeneity parameters unavailable from standard approaches.
Conclusions: Extreme heterogeneity constitutes the norm in global prevalence meta-analyses. The underutilization of prediction intervals (5.8%) and meta-regression (34.0%) represents the critical gap for improving statistical rigor. An exploratory Bayesian analysis demonstrated concordance with frequentist estimates, while providing additional uncertainty quantification. This illustrates that alternative methods are feasible, though their value lies primarily in specific scenarios rather than routine application. Prevalence-specific frameworks should recognize high heterogeneity as an expected characteristic requiring comprehensive exploration rather than elimination.
References
Borges Migliavaca C, Stein C, Colpani V, Barker TH, Munn Z, Falavigna M, et al. How are systematic reviews of prevalence conducted? A methodological study. BMC Med Res Methodol 2020; 20(1): 96. DOI: https://doi.org/10.1186/s12874-020-00975-3
Barker TH, Migliavaca CB, Stein C, Colpani V, Falavigna M, Aromataris E, et al. Conducting proportional meta-analysis in different types of systematic reviews: a guide for synthesisers of evidence. BMC Med Res Methodol 2021; 21(1): 189. DOI: https://doi.org/10.1186/s12874-021-01381-z
Migliavaca CB, Stein C, Colpani V, Barker TH, Ziegelmann PK, Munn Z, et al. Meta-analysis of prevalence: I2 statistic and how to deal with heterogeneity. Res Synth Methods 2022; 13(3): 363-7. DOI: https://doi.org/10.1002/jrsm.1547
Munn Z, Stern C, Aromataris E, Lockwood C, Jordan Z. What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Med Res Methodol 2018; 18(1): 5. DOI: https://doi.org/10.1186/s12874-017-0468-4
Schwarzer G, Chemaitelly H, Abu-Raddad LJ, Rücker G. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods 2019; 10(3): 476-83. DOI: https://doi.org/10.1002/jrsm.1348
Ioannidis JPA. Interpretation of tests of heterogeneity and bias in meta-analysis. J Eval Clin Pract 2008; 14(5): 951-7. DOI: https://doi.org/10.1111/j.1365-2753.2008.00986.x
Higgins JPT. Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epidemiol 2008; 37(5): 1158-60. DOI: https://doi.org/10.1093/ije/dyn204
Rücker G, Schwarzer G, Carpenter JR, Schumacher M. Undue reliance on I(2) in assessing heterogeneity may mislead. BMC Med Res Methodol 2008; 8: 79. DOI: https://doi.org/10.1186/1471-2288-8-79
Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327(7414): 557-60. DOI: https://doi.org/10.1136/bmj.327.7414.557
Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J Epidemiol Community Health 2013; 67(11): 974-8. DOI: https://doi.org/10.1136/jech-2013-203104
IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open 2016; 6(7): e010247.
Gates M, Gates A, Pieper D, Fernandes RM, Tricco AC, Moher D, et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ 2022; 378: e070849. DOI: https://doi.org/10.1136/bmj-2022-070849
Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 2017; 358: j4008. DOI: https://doi.org/10.1136/bmj.j4008
Röver C. Bayesian random-effects meta-analysis using the bayesmeta R package. J Stat Softw 2020; 93(6): 1-51. DOI: https://doi.org/10.18637/jss.v093.i06
Polson NG, Scott JG. On the half-Cauchy prior for a global scale parameter. Bayesian Anal 2012; 7(4): 887-902. DOI: https://doi.org/10.1214/12-BA730
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. J Stat Softw 2017; 76(1): 1-32. DOI: https://doi.org/10.18637/jss.v076.i01
Albadrani MS, Tobaiqi MA, Muaddi MA, Eltahir HM, Abdoh ES, Aljohani AM, et al. A global prevalence of electronic nicotine delivery systems (ENDS) use among students: a systematic review and meta-analysis of 4,189,145 subjects. BMC Public Health 2024; 24(1): 3311. DOI: https://doi.org/10.1186/s12889-024-20858-2
Wikswo ME, Kambhampati AK, Mattison CP, Chhabra P, Olojo O, Rana T, et al. A systematic review and meta-analysis of the global prevalence of human enteric adenovirus infections. J Infect Public Health 2025; 18(7): 102800. DOI: https://doi.org/10.1016/j.jiph.2025.102800
Kinner SA, Snow K, Wirtz AL, Altice FL, Beyrer C, Dolan K. Age-Specific Global Prevalence of Hepatitis B, Hepatitis C, HIV, and Tuberculosis Among Incarcerated People: A Systematic Review. J Adolesc Health 2018; 62(3S): S18-26. DOI: https://doi.org/10.1016/j.jadohealth.2017.09.030
Wei J, Zhu X, Liu J, Gao Y, Liu X, Wang K, et al. Estimating global prevalence of mild cognitive impairment and dementia in elderly with overweight, obesity, and central obesity: A systematic review and meta-analysis. Obes Rev 2025; 26(5): e13882. DOI: https://doi.org/10.1111/obr.13882
Al Wachami N, Guennouni M, Iderdar Y, Boumendil K, Arraji M, Mourajid Y, et al. Estimating the global prevalence of chronic obstructive pulmonary disease (COPD): a systematic review and meta-analysis. BMC Public Health 2024; 24(1): 297. DOI: https://doi.org/10.1186/s12889-024-17686-9
Adeloye D, Chua S, Lee C, Basquill C, Papana A, Theodoratou E, et al. Global and regional estimates of COPD prevalence: Systematic review and meta-analysis. J Glob Health 2015; 5(2): 020415. DOI: https://doi.org/10.7189/jogh.05.020415
Xie X, Pei J, Zhang L, Wu Y. Global birth prevalence of major congenital anomalies: a systematic review and meta-analysis. BMC Public Health 2025; 25(1): 449. DOI: https://doi.org/10.1186/s12889-025-21642-6
Song P, Xu Y, Zha M, Zhang Y, Rudan I. Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors. J Glob Health 2025; 9(1): 010427. DOI: https://doi.org/10.7189/jogh.09.010427
Fall A, Kenmoe S, Ebogo-Belobo JT, Mbaga DS, Bowo-Ngandji A, Foe-Essomba JR, et al. Global prevalence and case fatality rate of Enterovirus D68 infections, a systematic review and meta-analysis. PLoS Negl Trop Dis 2022; 16(2): e0010073. DOI: https://doi.org/10.1371/journal.pntd.0010073
Sulaiman SK, Isma’il Tsiga-Ahmed F, Musa MS, Makama BT, Sulaiman AK, Abdulaziz TB. Global prevalence and correlates of mpox vaccine acceptance and uptake: a systematic review and meta-analysis. Commun Med (Lond) 2024; 4: 136. DOI: https://doi.org/10.1038/s43856-024-00564-1
Suleiman AS, Bhattacharya P, Islam MA. Global prevalence and dynamics of mecA and mecC genes in MRSA: Meta-meta-analysis, meta-regression, and temporal investigation. J Infect Public Health 2025; 18(7): 102802. DOI: https://doi.org/10.1016/j.jiph.2025.102802
Hu X, Chen Y, Shen Y, Tian R, Sheng Y, Que H. Global prevalence and epidemiological trends of Hashimoto’s thyroiditis in adults: A systematic review and meta-analysis. Front Public Health 2022; 10: 1020709. DOI: https://doi.org/10.3389/fpubh.2022.1020709
White SJ, Sin J, Sweeney A, Salisbury T, Wahlich C, Montesinos Guevara CM, et al. Global Prevalence and Mental Health Outcomes of Intimate Partner Violence Among Women: A Systematic Review and Meta-Analysis. Trauma, Violence, & Abuse 2024; 25(1): 494-511. DOI: https://doi.org/10.1177/15248380231155529
Fajar JK, Sallam M, Soegiarto G, Sugiri YJ, Anshory M, Wulandari L, et al. Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitancy: A Meta-Analysis. Vaccines (Basel) 2022; 10(8): 1356. DOI: https://doi.org/10.3390/vaccines10081356
Delie AM, Bogale EK, Anagaw TF, Tiruneh MG, Fenta ET, Adal O, et al. Global prevalence and predictors of scabies among prisoners: systematic review and meta-analysis. BMC Public Health 2024; 24(1): 1894. DOI: https://doi.org/10.1186/s12889-024-19401-0
Syed S, Ashwick R, Schlosser M, Jones R, Rowe S, Billings J. Global prevalence and risk factors for mental health problems in police personnel: a systematic review and meta-analysis. Occup Environ Med 2020; 77(11): 737-47. DOI: https://doi.org/10.1136/oemed-2020-106498
Alshehri AA, Irekeola AA. Global prevalence of alkhumra hemorrhagic fever virus infection: The first meta-analysis and systematic review. J Infect Public Health 2024; 17(6): 986-93. DOI: https://doi.org/10.1016/j.jiph.2024.04.001
Holland C, Sepidarkish M, Deslyper G, Abdollahi A, Valizadeh S, Mollalo A, et al. Global prevalence of Ascaris infection in humans (2010-2021): a systematic review and meta-analysis. Infect Dis Poverty 2022; 11(1): 113. DOI: https://doi.org/10.1186/s40249-022-01038-z
Whitten T, Tzoumakis S, Green MJ, Dean K. Global Prevalence of Childhood Exposure to Physical Violence within Domestic and Family Relationships in the General Population: A Systematic Review and Proportional Meta-Analysis. Trauma Violence Abuse 2024; 25(2): 1411-30. DOI: https://doi.org/10.1177/15248380231179133
Ukwishaka J, Ndayishimiye Y, Destine E, Danwang C, Kirakoya-Samadoulougou F. Global prevalence of coronavirus disease 2019 reinfection: a systematic review and meta-analysis. BMC Public Health 2023; 23(1): 778. DOI: https://doi.org/10.1186/s12889-023-15626-7
Salari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis. Infectious Diseases of Poverty 2023; 12(1): 57. DOI: https://doi.org/10.1186/s40249-023-01107-x
Zhang P, Hao C, Di X, Chuizhao X, Jinsong L, Guisen Z, et al. Global prevalence of norovirus gastroenteritis after emergence of the GII.4 Sydney 2012 variant: a systematic review and meta-analysis. Front Public Health 2024; 12: 1373322. DOI: https://doi.org/10.3389/fpubh.2024.1373322
Shafiee A, Nakhaee Z, Bahri RA, Amini MJ, Salehi A, Jafarabady K, et al. Global prevalence of obesity and overweight among medical students: a systematic review and meta-analysis. BMC Public Health 2024; 24(1): 1673. DOI: https://doi.org/10.1186/s12889-024-19184-4
Li Y-J, Xie X-N, Lei X, Li Y-M, Lei X. Global prevalence of obesity, overweight and underweight in children, adolescents and adults with autism spectrum disorder, attention-deficit hyperactivity disorder: A systematic review and meta-analysis. Obes Rev 2020; 21(12): e13123. DOI: https://doi.org/10.1111/obr.13123
Tolera ST, Gobena T, Assefa N, Geremew A. Global prevalence of occupational injuries among sanitation workers: a systematic review and meta-analysis. Front Public Health 2024; 12: 1425904. DOI: https://doi.org/10.3389/fpubh.2024.1425904
Taher MK, Salzman T, Banal A, Morissette K, Domingo FR, Cheung AM, et al. Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence. Health Promot Chronic Dis Prev Can 2025; 45(3): 112-38. DOI: https://doi.org/10.24095/hpcdp.45.3.02
Sheng Y, Jin L-Y, Li N, Zhang Y, Shi Y-J. Global prevalence of psittacosis in outbreaks: a systematic review and meta-analysis. BMC Public Health 2025; 25(1): 2010. DOI: https://doi.org/10.1186/s12889-025-21612-y
Engku Abd Rahman ENS, Irekeola AA, Elmi AH, Chua WC, Chan YY. Global prevalence patterns and distribution of Vibrio cholerae: A systematic review and meta-analysis of 176,740 samples. J Infect Public Health 2024; 17(11): 102558. DOI: https://doi.org/10.1016/j.jiph.2024.102558
Niu X, Zhu L, Xu Y, Zhang M, Hao Y, Ma L, et al. Global prevalence, incidence, and outcomes of alcohol related liver diseases: a systematic review and meta-analysis. BMC Public Health 2023; 23(1): 859. DOI: https://doi.org/10.1186/s12889-023-15749-x
Fang Y, Liu F, Zhang X, Chen L, Liu Y, Yang L, et al. Mapping global prevalence of menopausal symptoms among middle-aged women: a systematic review and meta-analysis. BMC Public Health 2024; 24(1): 1767. DOI: https://doi.org/10.1186/s12889-024-19280-5
Renzi E, Baccolini V, Migliara G, Bellotta C, Ceparano M, Donia P, et al. Mapping the Prevalence of COVID-19 Vaccine Acceptance at the Global and Regional Level: A Systematic Review and Meta-Analysis. Vaccines (Basel) 2022; 10(9): 1488. DOI: https://doi.org/10.3390/vaccines10091488
Jafari A, Rajabi A, Gholian-Aval M, Peyman N, Mahdizadeh M, Tehrani H. National, regional, and global prevalence of cigarette smoking among women/females in the general population: a systematic review and meta-analysis. Environ Health Prev Med 2021; 26(1): 5. DOI: https://doi.org/10.1186/s12199-020-00924-y
Bouqoufi A, Laila L, Boujraf S, Hadj FAE, Razine R, Abouqal R, et al. Prevalence and associated factors of self-medication in worldwide pregnant women: systematic review and meta-analysis. BMC Public Health 2024; 24(1): 308. DOI: https://doi.org/10.1186/s12889-023-17195-1
Dagnaw M, Muche AA, Geremew BM, Gezie LD. Prevalence and burden of HBV-HIV co-morbidity: a global systematic review and meta-analysis. Front Public Health 2025; 13: 1565621. DOI: https://doi.org/10.3389/fpubh.2025.1565621
Muche AA, Olayemi OO, Gete YK. Prevalence and determinants of gestational diabetes mellitus in Africa based on the updated international diagnostic criteria: a systematic review and meta-analysis. Arch Public Health 2019; 77: 36. DOI: https://doi.org/10.1186/s13690-019-0362-0
Dillard LK, Arunda MO, Lopez-Perez L, Martinez RX, Jiménez L, Chadha S. Prevalence and global estimates of unsafe listening practices in adolescents and young adults: a systematic review and meta-analysis. BMJ Glob Health 2022; 7(11): e010501. DOI: https://doi.org/10.1136/bmjgh-2022-010501
Pan Y, Lin X, Liu J, Zhang S, Zeng X, Chen F, et al. Prevalence of Childhood Sexual Abuse Among Women Using the Childhood Trauma Questionnaire: A Worldwide Meta-Analysis. Trauma Violence Abuse 2021; 22(5): 1181-91. DOI: https://doi.org/10.1177/1524838020912867
Escobar N, Plugge E. Prevalence of human papillomavirus infection, cervical intraepithelial neoplasia and cervical cancer in imprisoned women worldwide: a systematic review and meta-analysis. J Epidemiol Community Health 2020; 74(1): 95-102. DOI: https://doi.org/10.1136/jech-2019-212557
Kip A, Valencia S, Glunz E, Lowe SR, Tam K-P, Morina N. Prevalence of mental disorders in adult populations from the Global South following exposure to natural hazards: a meta-analysis. Epidemiol Psychiatr Sci 2024; 33: e68. DOI: https://doi.org/10.1017/S2045796024000672
Alemayehu TT, Wassie YA, Bekalu AF, Tegegne AA, Ayenew W, Tadesse G, et al. Prevalence of potential drug-drug interactions and associated factors among elderly patients in Ethiopia: a systematic review and meta-analysis. Glob Health Res Policy 2024; 9(1): 46. DOI: https://doi.org/10.1186/s41256-024-00386-7
Zheng Y, Ye K, Ying M, He Y, Yu Q, Lan L, et al. Syphilis epidemic among men who have sex with men: A global systematic review and meta-analysis of prevalence, incidence, and associated factors. J Glob Health 2024; 14: 04004. DOI: https://doi.org/10.7189/jogh.14.04004
Faustino R, Faria M, Teixeira M, Palavra F, Sargento P, do Céu Costa M. Systematic review and meta-analysis of the prevalence of coronavirus: One health approach for a global strategy. One Health 2022; 14: 100383. DOI: https://doi.org/10.1016/j.onehlt.2022.100383
Ahmadi Gharaei H, Fararouei M, Mirzazadeh A, Sharifnia G, Rohani-Rasaf M, Bastam D, et al. The global and regional prevalence of hepatitis C and B co-infections among prisoners living with HIV: a systematic review and meta-analysis. Infect Dis Poverty 2021; 10(1): 93. DOI: https://doi.org/10.1186/s40249-021-00876-7
Song P, Wu J, Cao J, Sun W, Li X, Zhou T, et al. The global and regional prevalence of restless legs syndrome among adults: A systematic review and modelling analysis. J Glob Health 2024; 14: 04113. DOI: https://doi.org/10.7189/jogh.14.04113
Shariati A, Dadashi M, Chegini Z, van Belkum A, Mirzaii M, Khoramrooz SS, et al. The global prevalence of Daptomycin, Tigecycline, Quinupristin/Dalfopristin, and Linezolid-resistant Staphylococcus aureus and coagulase-negative staphylococci strains: a systematic review and meta-analysis. Antimicrob Resist Infect Control 2020; 9(1): 56. DOI: https://doi.org/10.1186/s13756-020-00714-9
Hajikhani B, Goudarzi M, Kakavandi S, Amini S, Zamani S, van Belkum A, et al. The global prevalence of fusidic acid resistance in clinical isolates of Staphylococcus aureus: a systematic review and meta-analysis. Antimicrob Resist Infect Control 2021; 10(1): 75. DOI: https://doi.org/10.1186/s13756-021-00943-6
Hasanpour AH, Sepidarkish M, Mollalo A, Ardekani A, Almukhtar M, Mechaal A, et al. The global prevalence of methicillin-resistant Staphylococcus aureus colonization in residents of elderly care centers: a systematic review and meta-analysis. Antimicrob Resist Infect Control 2023; 12(1): 4. DOI: https://doi.org/10.1186/s13756-023-01210-6
Armoon B, Mohammadi R, Griffiths MD. The Global Prevalence of Non-suicidal Self-injury, Suicide Behaviors, and Associated Risk Factors Among Runaway and Homeless Youth: A Meta-analysis. Community Ment Health J 2024; 60(5): 919-44. DOI: https://doi.org/10.1007/s10597-024-01245-y
Salari N, Heidarian P, Abdolmaleki A, Salim K, Hashemian SH, Daneshkhah A, et al. The global prevalence of single-child families with emphasis on influential factors: a comprehensive systematic review and meta-analysis. Popul Health Metr 2025; 23(1): 25. DOI: https://doi.org/10.1186/s12963-025-00393-x
Behniafar H, Sepidarkish M, Tadi MJ, Valizadeh S, Gholamrezaei M, Hamidi F, et al. The global prevalence of Trichuris trichiura infection in humans (2010-2023): A systematic review and meta-analysis. J Infect Public Health 2024; 17(5): 800-9. DOI: https://doi.org/10.1016/j.jiph.2024.03.005
Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I, et al. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health 2021; 11: 04009. DOI: https://doi.org/10.7189/jogh.11.04009
Dai X, Chu X, Qi G, Yuan P, Zhou Y, Xiang H, et al. Worldwide Perinatal Intimate Partner Violence Prevalence and Risk Factors for Post-traumatic Stress Disorder in Women: A Systematic Review and Meta-analysis. Trauma Violence Abuse 2024; 25(3): 2363-76. DOI: https://doi.org/10.1177/15248380231211950
Román-Gálvez RM, Martín-Peláez S, Fernández-Félix BM, Zamora J, Khan KS, Bueno-Cavanillas A. Worldwide Prevalence of Intimate Partner Violence in Pregnancy. A Systematic Review and Meta-Analysis. Front Public Health 2021; 9: 738459. DOI: https://doi.org/10.3389/fpubh.2021.738459
Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc 2015; 13(3): 147-53. DOI: https://doi.org/10.1097/XEB.0000000000000054
Riley RD, Higgins JPT, Deeks JJ. Interpretation of random effects meta-analyses. BMJ 2011; 342: d549. DOI: https://doi.org/10.1136/bmj.d549
Partlett C, Riley RD. Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation. Stat Med 2017; 36(2): 301-17. DOI: https://doi.org/10.1002/sim.7140
Nagashima K, Noma H, Furukawa TA. Prediction intervals for random-effects meta-analysis: A confidence distribution approach. Stat Methods Med Res 2019; 28(6): 1689-702. DOI: https://doi.org/10.1177/0962280218773520
Cordero CP, Dans AL. Key concepts in clinical epidemiology: detecting and dealing with heterogeneity in meta-analyses. J Clin Epidemiol 2021; 130: 149-51. DOI: https://doi.org/10.1016/j.jclinepi.2020.09.045
Hoffmann F, Eggers D, Pieper D, Zeeb H, Allers K. An observational study found large methodological heterogeneity in systematic reviews addressing prevalence and cumulative incidence. J Clin Epidemiol 2020; 119: 92-9. DOI: https://doi.org/10.1016/j.jclinepi.2019.12.003
Ades AE, Lu G, Higgins JPT. The interpretation of random-effects meta-analysis in decision models. Med Decis Making 2005; 25(6): 646-54. DOI: https://doi.org/10.1177/0272989X05282643
Deeks JJ, Higgins JPT, Altman DG, McKenzie JE, Veroniki AA, editors. Chapter 10: Analysing data and undertaking meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions 2024.
IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis 2016 [cited 9 November 2025]. DOI: https://doi.org/10.1136/bmjopen-2015-010247
Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21(11): 1539-58. DOI: https://doi.org/10.1002/sim.1186
Spineli LM, Pandis N. Prediction interval in random-effects meta-analysis. Am J Orthod Dentofacial Orthop 2020; 157(4): 586-8. DOI: https://doi.org/10.1016/j.ajodo.2019.12.011
Borenstein M. How to understand and report heterogeneity in a meta-analysis: The difference between I-squared and prediction intervals. Integr Med Res 2023; 12(4): 101014. DOI: https://doi.org/10.1016/j.imr.2023.101014
Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw 2010; 36(3): 1-48. DOI: https://doi.org/10.18637/jss.v036.i03
Harris RJ, Bradburn MJ, Deeks JJ, Harbord RM, Altman DG, Sterne JAC. Metan: fixed- and random-effects meta-analysis. Stata J 2008; 8(1): 3-28. DOI: https://doi.org/10.1177/1536867X0800800102
Thompson SG, Higgins JPT. How should meta-regression analyses be undertaken and interpreted? Stat Med 2002; 21(11): 1559-73. DOI: https://doi.org/10.1002/sim.1187
Patsopoulos NA, Evangelou E, Ioannidis JPA. Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol 2008; 37(5): 1148-57. DOI: https://doi.org/10.1093/ije/dyn065
Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010; 340: c221. DOI: https://doi.org/10.1136/bmj.c221
Sutton AJ, Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res 2001; 10(4): 277-303. DOI: https://doi.org/10.1191/096228001678227794
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley & Sons; 2004. DOI: https://doi.org/10.1002/0470092602
Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 2016; 7(1): 55-79. DOI: https://doi.org/10.1002/jrsm.1164
Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med 2005; 24(15): 2401-28. DOI: https://doi.org/10.1002/sim.2112
Bürkner PC. brms: An R package for Bayesian multilevel models using Stan. J Stat Softw 2017; 80(1): 1-28. DOI: https://doi.org/10.18637/jss.v080.i01
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986; 7(3): 177-88. DOI: https://doi.org/10.1016/0197-2456(86)90046-2
IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol 2014; 14: 25. DOI: https://doi.org/10.1186/1471-2288-14-25
Röver C, Friede T. Using the bayesmeta R package for Bayesian random-effects meta-regression. Comput Methods Programs Biomed 2023; 229: 107303. DOI: https://doi.org/10.1016/j.cmpb.2022.107303
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; n71. DOI: https://doi.org/10.1136/bmj.n71
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
