On the exact null-distribution of a test for homogeneity of the risk ratio in meta-analysis of studies with rare events

References

• Böhning D, Mylona K, Kimber A. Meta-analysis of clinical trials with rare events. Biom J. 2015;57(4):633–648.
   PubMed Web of Science ®Google Scholar
• Schiff JH, Welker A, Fohr B, et al. Major incidents and complications in otherwise healthy patients undergoing elective procedures: results based on 1.37 million anaesthetic procedures. Br J Anaesth. 2014;113(1):109–121.
   PubMed Web of Science ®Google Scholar
• Schulze R, Holling H, Böhning D, editors. Meta-analysis: new developments and applications in medical and social sciences. Cambridge (MA): Hogrefe and Huber Publishing; 2003.
   Google Scholar
• Borenstein M, Hedges LV, Higgins JP, et al., editors. Introduction to meta-analysis. Chichester: Wiley; 2009.
   Google Scholar
• Sweeting MJ, Sutton AJ, Lambert PC. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Stat Med. 2004;23(9):1351–1375.
   PubMed Web of Science ®Google Scholar
• Bradburn MJ, Deeks JJ, Berlin JA, et al. Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Stat Med. 2007;26(1):53–77.
   PubMed Web of Science ®Google Scholar
• Keus F, Wetterslev J, Gluud C, et al. Robustness assessments are needed to reduce bias in meta-analyses that include zero-event randomized trials. Am J Gastroenterol. 2009;104(3):546–551.
   PubMed Web of Science ®Google Scholar
• Kuss O. Statistical methods for meta-analyses including information from studies without any events -- add nothing to nothing and succeed nevertheless. Stat Med. 2015;34(7):1097–1116.
   PubMed Web of Science ®Google Scholar
• Efthimiou O. Practical guide to the meta-analysis of rare events. Evid-Based Ment Health. 2018;21(2):72–76.
   PubMed Web of Science ®Google Scholar
• Jackson D, White IR. When should meta-analysis avoid making hidden normality assumptions? Biom J. 2018;60(6):1040–1058.
   PubMed Web of Science ®Google Scholar
• Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):9–48.
   Google Scholar
• Casella G, Berger RL, editors. Statistical inference. Pacific Grove: Duxbury Press; 2002.
   Google Scholar
• Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–1558.
   PubMed Web of Science ®Google Scholar
• Almalik O, Heuvel ER. Testing homogeneity of effect sizes in pooling 2×2 contingency tables from multiple studies: a comparison of methods. Cogent Math Stat. 2018;5(1):1–18.
   Google Scholar
• Cochran W. Some methods for strengthening the common χ2 tests. Biometrics. 1954;10(4):417–451.
   Web of Science ®Google Scholar
• Higgins JPT, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560.
   PubMed Web of Science ®Google Scholar
• Crowley P. Interventions for preventing or improving the outcome of delivery at or beyond term. Cochrane Database Syst Rev. 2000;2:CD000170.
   Google Scholar
• Sánchez-Meca J, Marán-Martánez F. Homogeneity tests in meta-analysis: a Monte Carlo comparison of statistical power and type I error. Qual Quant. 1997;31(4):385–399.
   Google Scholar
• Sterne JAC, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001;54(10):1046–1055.
   PubMed Web of Science ®Google Scholar
• Viechtbauer W. Hypothesis tests for population heterogeneity in meta-analysis. Br J Math Stat Psychol. 2007;60(1):29–60.
   PubMed Web of Science ®Google Scholar
• Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:1–6.
   Google Scholar
• Kulinskaya E, Dollinger MB. An accurate test for homogeneity of odds ratios based on Cochran's Q statistic. BMC Med Res Methodol. 2015;15(49):1–19.
   PubMedGoogle Scholar
• Stijnen T, Hamza TH, Ozdemir P. Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Stat Med. 2010;29(29):3046–367.
   PubMed Web of Science ®Google Scholar
• Friede T, Röver C, Wandel S, et al. Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases. Biom J. 2017;59(4):658–671.
   PubMed Web of Science ®Google Scholar
• R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2019.
   Google Scholar