Abstract
Studies within a systematic review are often combined statistically in a meta-analysis, which quantitatively synthesizes all available evidence about the relative effects of two healthcare interventions for the same clinical outcome. A key issue in every meta-analysis is whether the identified randomized control trials (RCTs) are similar enough to be combined together since important differences in trial- or patient-level characteristics may affect the treatment effects. Such differences, called heterogeneity, need to be properly investigated and accounted for in the analysis. In this chapter, we introduce the basic concepts of meta-analyses of RCTs and we describe the two main meta-analytical models, namely, the common effect and the random effects models. Then, we present several ways to identify and assess heterogeneity. We discuss the interpretation of results from a meta-analysis using two exemplar datasets. The chapter closes with a brief introduction to more sophisticated meta-analytical techniques such as the use of individual participant data and network meta-analysis.
References
Borenstein M, Higgins JP (2013) Meta-analysis and subgroups. Prev Sci 14(2):134–143
Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1(2):97–111
Borenstein M, Higgins JP, Hedges LV, Rothstein HR (2017) Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Res Synth Methods 8(1):5–18
Brunoni AR, Chaimani A, Moffa AH et al (2017) Repetitive transcranial magnetic stimulation for the acute treatment of major depressive episodes: a systematic review with network meta-analysis. JAMA Psychiat 74(2):143–152
Caldwell DM, Ades AE, Higgins JPT (2005) Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 331(7521):897–900
Chaimani A, Caldwell DM, Li T, Higgins JP, Salanti G (2019) Undertaking network meta-analyses. In: Cochrane handbook for systematic reviews of interventions. Wiley, pp 285–320. https://doi.org/10.1002/9781119536604.ch11
Debray TP, Moons KG, van Valkenhoef G et al (2015) Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods 6(4):293–309
Deeks JJ, Higgins JP, Altman DG, Group CSM (2019) Analysing data and undertaking meta-analyses. In: Cochrane handbook systematic reviews of interventions. John Wiley & Sons, Ltd; Published online 2019, pp 241–284. https://doi.org/10.1002/9781119536604.ch10
DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188
Duval S (2005) The trim and fill method. In: Publication bias in meta-analysis prevention assessment and adjustments. John Wiley & Sons, Ltd; Published online, pp 127–144. https://doi.org/10.1002/0470870168.ch8
Efthimiou O (2018) Practical guide to the meta-analysis of rare events. Evid Based Ment Health 21(2):72. https://doi.org/10.1136/eb-2018-102911
Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634
Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558
Higgins JP, Thomas J, Chandler J et al (2019) Cochrane handbook for systematic reviews of interventions. Wiley, Chichester
Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC (1992) Cumulative meta-analysis of therapeutic trials for myocardial infarction. N Engl J Med 327(4):248–254. https://doi.org/10.1056/NEJM199207233270406
Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22(4):719–748
Mavridis D, Salanti G (2014) Exploring and accounting for publication bias in mental health: a brief overview of methods. Evid Based Ment Health 17(1):11. https://doi.org/10.1136/eb-2013-101700
Mavridis D, Chaimani A, Efthimiou O, Leucht S, Salanti G (2014) Addressing missing outcome data in meta-analysis. Evid Based Ment Health 17(3):85–89
Mavridis D, Giannatsi M, Cipriani A, Salanti G (2015) A primer on network meta-analysis with emphasis on mental health. Evid Based Ment Health 18(2):40–46
Moreno SG, Sutton AJ, Turner EH et al (2009a) Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications. BMJ b2981:339
Moreno SG, Sutton AJ, Ades AE et al (2009b) Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. BMC Med Res Methodol 9(1):2
Moreno SG, Sutton AJ, Ades AE, Cooper NJ, Abrams KR (2011) Adjusting for publication biases across similar interventions performed well when compared with gold standard data. J Clin Epidemiol 64(11):1230–1241
Nikolakopoulou A, Mavridis D, Salanti G (2014) Demystifying fixed and random effects meta-analysis. Evid Based Ment Health 17(2):53. https://doi.org/10.1136/eb-2014-101795
Paule RC, Mandel J (1982) Consensus values and weighting factors. J Res Natl Bur Stand 87(5):377–385
Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L (2006) Comparison of two methods to detect publication bias in meta-analysis. JAMA 295(6):676–680
Petropoulou M, Mavridis D (2017) A comparison of 20 heterogeneity variance estimators in statistical synthesis of results from studies: a simulation study. Stat Med 36(27):4266–4280
Pildal J, Hrobjartsson A, Jørgensen KJ, Hilden J, Altman DG, Gøtzsche PC (2007) Impact of allocation concealment on conclusions drawn from meta-analyses of randomized trials. Int J Epidemiol 36(4):847–857
Rhodes KM, Turner RM, Higgins JPT (2015) Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J Clin Epidemiol 68(1):52–60. https://doi.org/10.1016/j.jclinepi.2014.08.012
Salanti G (2012) Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods 3(2):80–97
Savović J, Jones HE, Altman DG et al (2012) Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of meta-epidemiological studies. Health Technol Assess 16(35):1–82
Sidik K, Jonkman JN (2007) A comparison of heterogeneity variance estimators in combining results of studies. Stat Med 26(9):1964–1981
Thomas D, Radji S, Benedetti A (2014) Systematic review of methods for individual patient data meta-analysis with binary outcomes. BMC Med Res Methodol 14(1):79
Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP (2012) Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol 41(3):818–827. https://doi.org/10.1093/ije/dys041
Turner RM, Bird SM, Higgins JP (2013) The impact of study size on meta-analyses: examination of underpowered studies in Cochrane reviews. PLoS One 8(3):e59202
Veroniki AA, Jackson D, Viechtbauer W et al (2016) Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 7(1):55–79. https://doi.org/10.1002/jrsm.1164
Yusuf S, Peto R, Lewis J, Collins R, Sleight P (1985) Beta blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis 27(5):335–371
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Evrenoglou, T., Metelli, S., Chaimani, A. (2021). Introduction to Meta-Analysis. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52677-5_287-1
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DOI: https://doi.org/10.1007/978-3-319-52677-5_287-1
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