Meta-analysis is an approach to formally, systematically and quantitatively analyze multiple existing research studies and to synthesize new research findings based upon the existing data. Until the late 1970s, meta-analyses were not regularly reported in the medical literature, but since then there has been an exponential growth of meta-analyses and they are now among the most frequently cited form of research. A properly performed systematic review and meta-analysis is a very important tool in evidence-based medicine and a good understanding of the steps involved in doing a systematic review and meta-analysis is important to yield meaningful results. The purpose of this review article is to provide a brief overview about systematic reviews and meta-analyses and the underlying principles for conducting this type of research. Methodological approaches for conducting a meticulous meta-analysis are described and the important steps involved in the interpretation and presentation of meta-analysis are outlined and discussed. The key objective of this paper is to outline a step-by-step approach that is useful to all researchers, who would like to conduct their first meta-analysis. This paper also provides clinicians and researchers with the information to interpret systematic reviews and meta-analyses.
Report on Certain Enteric Fever Inoculation Statistics. Br Med J. 1904;2:1243-6.
O’Rourke K. An historical perspective on meta-analysis: dealing quantitatively with varying study results. J R Soc Med. 2007;100:579–82.
[DOI: 10.1177/0141076807100012020]
Glass GV. Primary, secondary, and meta-analysis of research. Educ Res. 1976;5:3–8.
Higgins JPT, Chandler JTJ, Cumpston M, et al. Cochrane handbook for systematic reviews of interventions. Chichester (UK): John Wiley & Sons; 2019.
Rahman MM, Ghoshal UC, Ragunath K, et al. Biomedical research in developing countries: Opportunities, methods, and challenges. Indian J Gastroenterol. 2020;39:292-302.
Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.
Chalmers I. The Cochrane collaboration: preparing, maintaining, and disseminating systematic reviews of the effects of health care. Ann N Y Acad Sci. 1993;703:156–65.
[DOI: 10.1111/j.1749-6632.1993.tb26345.x]
Tudur Smith C, Marcucci M, Nolan SJ, et al. Individual participant data meta-analyses compared with meta-analyses based on aggregate data. Cochrane Database Syst Rev. 2016;9:MR000007.
Brittain EH, Fay MP, Follmann DA. A valid formulation of the analysis of noninferiority trials under random effects meta-analysis. Biostatistics. 2012;13:637-49.
Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. Improving the Quality of Reports of Meta-Analyses of Randomised Controlled Trials: The QUOROM Statement. Onkologie. 2000;23:597-602.
Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008-12.
Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med. 2009;151:W65-94.
Dekkers OM, Vandenbroucke JP, Cevallos M, Renehan AG, Altman DG, Egger M. COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology. PLoS Med. 2019;16:e1002742.
Shea BJ, Reeves BC, Wells G, 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.
Sanderson S, Tatt ID, Higgins JP. Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography. Int J Epidemiol. 2007;36:666-76.
Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.
Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. 2007;335:914-6.
Light RJ, Pillemer DB. Summing up. The science of reviewing research. Harvard University Press, Cambridge, Mass., 1984.
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629-34.
Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001;54:1046–55.
[DOI: 10.1016/S0895-4356(01)00377-8]
Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14 Suppl 1:29–37.
[PMID: 21487488]
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629-34.
Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med. 2002;21:1559-73.
Bax L, Yu LM, Ikeda N, Moons KG. A systematic comparison of software dedicated to meta-analysis of causal studies. BMC Med Res Methodol. 2007;7:40.
Koricheva J, Gurevitch J, Mengersen K. Handbook of meta-analysis in ecology and evolution: Princeton University Press; 2013.
Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive meta-analysis version 2. Englewood, NJ: Biostat. 2005;104.
Cheung MW. metaSEM: an R package for meta-analysis using structural equation modeling. Front Psychol. 2015;5:1521. Cheung MW-L. metaSEM: an R package for meta-analysis using structural equation modeling. 2015. 5(1521).
Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008 ;336:924-6.
Sideri S, Papageorgiou SN, Eliades T. Registration in the international prospective register of systematic reviews (PROSPERO) of systematic review protocols was associated with increased review quality. J Clin Epidemiol. 2018;100:103–10.
[DOI: 10.1016/j.jclinepi.2018.01.003]
Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ. 2001;322:1479-80.
Shah A, Talley NJ, Jones M, et al. Small intestinal bacterial overgrowth in irritable bowel syndrome: A systematic review and meta-analysis of case-control studies. Am J Gastroenterol. 2020;115:190-201.
