Meta-analysis of single rates with zero events_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Man ^1,2 ^# , CHEN Wensong ^1,2 ,  LIU Yuxiu ^1,2,3 , LIU Yaqi ^1,2 , YUAN Yangdan ^2,3

1. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, P.R.China;
2. Department of Critical Care Medicine, General Hospital of Eastern Theater Command, Nanjing 210002, P.R.China;
3. Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, P.R.China;

Corresponding?author：

LIU Yuxiu, Email: liu_yuxiu@163.com

Keywords：

Meta-analysis; Single rate; Zero events; Freeman-Tukey transformation

DOI：

10.7507/1672-2531.202005059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objectives To explore the statistical performance of different methods for meta-analysis of single rates with zero events so as to provide evidence for selecting meta-analysis methods in evidence-based research.Methods Through Monte-Carlo simulations, we evaluated the performance of various transformation and correction methods for meta-analysis of single rates with zero events, considering the point estimate bias, confidence interval coverage and width.Results When the overall event rate was above 30%, all methods showed good statistical performance. As the event rate dropped, log, logit, and arcsine transformations could still maintain good performance. However, when the event rate was less than 5%, only Freeman-Tukey transformation showed excellent performance.Conclusions The meta-analysis of single rate based on Freeman-Tukey transformation is robust, and should be recommended as the preferable choice of meta-analysis of single rate with zero events.

Citation： LIU Man, CHEN Wensong, LIU Yuxiu, LIU Yaqi, YUAN Yangdan. Meta-analysis of single rates with zero events. Chinese Journal of Evidence-Based Medicine, 2020, 20(10): 1226-1233. doi: 10.7507/1672-2531.202005059 Copy

1.	尹榕, 劉曌, 張國楨, 等. Wingspan支架治療顱內動脈粥樣硬化性狹窄單組率的Meta分析. 中國循證醫學雜志, 2015, 15(3): 295-304.
2.	夏青青, 王玥, 劉俐. 產前超聲篩查發現膽囊未顯示的胎兒不同結局發生率的Meta分析. 中國循證醫學雜志, 2019, 19(12): 1423-1429.
3.	郭淳, 王迎, 范允舟, 等. 中國狂犬病疫苗的抗體陽轉率的Meta分析. 中國循證醫學雜志, 2015, 15(10): 1207-1213.
4.	Leazer R, Perkins AM, Shomaker K, et al. A meta-analysis of the rates of listeria monocytogenes and enterococcus in febrile infants. Hosp Pediatr, 2016, 6(4): 187-195.
5.	Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol, 2008, 61(1): 41-51.
6.	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): 3046-3067.
7.	Barendregt JJ, Doi SA, Lee YY, et al. Meta-analysis of prevalence. J Epidemiol Community Health, 2013, 67(11): 974-978.
8.	Mosteller F, Youtz C. Tables of the Freeman-Tukey transformations for the binomial and Poisson distributions. Biometrika, 1961, 48: 433-440.
9.	Warton DI, Hui FK. The arcsine is asinine: the analysis of proportions in ecology. Ecology, 2011, 92(1): 3-10.
10.	Spittal MJ, Pirkis J, Gurrin LC. Meta-analysis of incidence rate data in the presence of zero events. BMC Med Res Method, 2015, 15(1): 15-42.
11.	Borenstein M, Hedges LV, Higgins JP, et al. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods, 2010, 1(2): 97-111.
12.	Miller JJ. The inverse of the Freeman-Tukey double arcsine transformation. Am Stat, 1978, 32(4): 138.
13.	Schwarzer G, Chemaitelly H, Abu-Raddad LJ, et al. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods, 2019, 10(3): 476-483.
14.	Whitehead A, Whitehead J. A general parametric approach to the meta-analysis of randomized clinical trials. Stat Med, 1991, 10(11): 1665-1677.
15.	Cook DJ, Witt LG, Cook RJ, et al. Stress ulcer prophylaxis in the critically ill: a meta-analysis. Am J Med, 1991, 91(5): 519-527.
16.	Chen DG, Peace KE. Applied meta-analysis with R (1st Edition). Boca Raton: Taylor & Francis Inc, 2013.
17.	羅美玲, 譚紅專, 周權, 等. 在R軟件中實現單個率的Meta分析. 循證醫學, 2013, 13(3): 181-184.
18.	Tebbs JM. An introduction to categorical data analysis (2nd Edition). New York: Wiley, 1996.
19.	劉關鍵, 吳泰相. 循證醫學中統計指標的正確應用. 中國臨床康復, 2003, 7(3): 359-362, 365.
20.	Newcombe RG. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med, 1998, 17(8): 857-872.
21.	Cheng J, Pullenayegum E, Marshall JK, et al. Impact of including or excluding both-armed zero-event studies on using standard meta-analysis methods for rare event outcome: a simulation study. BMJ Open, 2016, 6(8): 1-10.
22.	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.
23.	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.
24.	Steadman SS, Lisa AW, Michael JF, et al. An assessment of the use of the continuity correction for sparse data in meta-analysis. Commun Stat Simul Comput, 2014, 25(4): 1031-1056.
25.	Efthimiou O. Practical guide to the meta-analysis of rare events. Evid Based Ment Health, 2018, 21(2): 72-76.

1. 尹榕, 劉曌, 張國楨, 等. Wingspan支架治療顱內動脈粥樣硬化性狹窄單組率的Meta分析. 中國循證醫學雜志, 2015, 15(3): 295-304.
2. 夏青青, 王玥, 劉俐. 產前超聲篩查發現膽囊未顯示的胎兒不同結局發生率的Meta分析. 中國循證醫學雜志, 2019, 19(12): 1423-1429.
3. 郭淳, 王迎, 范允舟, 等. 中國狂犬病疫苗的抗體陽轉率的Meta分析. 中國循證醫學雜志, 2015, 15(10): 1207-1213.
4. Leazer R, Perkins AM, Shomaker K, et al. A meta-analysis of the rates of listeria monocytogenes and enterococcus in febrile infants. Hosp Pediatr, 2016, 6(4): 187-195.
5. Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol, 2008, 61(1): 41-51.
6. 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): 3046-3067.
7. Barendregt JJ, Doi SA, Lee YY, et al. Meta-analysis of prevalence. J Epidemiol Community Health, 2013, 67(11): 974-978.
8. Mosteller F, Youtz C. Tables of the Freeman-Tukey transformations for the binomial and Poisson distributions. Biometrika, 1961, 48: 433-440.
9. Warton DI, Hui FK. The arcsine is asinine: the analysis of proportions in ecology. Ecology, 2011, 92(1): 3-10.
10. Spittal MJ, Pirkis J, Gurrin LC. Meta-analysis of incidence rate data in the presence of zero events. BMC Med Res Method, 2015, 15(1): 15-42.
11. Borenstein M, Hedges LV, Higgins JP, et al. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods, 2010, 1(2): 97-111.
12. Miller JJ. The inverse of the Freeman-Tukey double arcsine transformation. Am Stat, 1978, 32(4): 138.
13. Schwarzer G, Chemaitelly H, Abu-Raddad LJ, et al. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods, 2019, 10(3): 476-483.
14. Whitehead A, Whitehead J. A general parametric approach to the meta-analysis of randomized clinical trials. Stat Med, 1991, 10(11): 1665-1677.
15. Cook DJ, Witt LG, Cook RJ, et al. Stress ulcer prophylaxis in the critically ill: a meta-analysis. Am J Med, 1991, 91(5): 519-527.
16. Chen DG, Peace KE. Applied meta-analysis with R (1st Edition). Boca Raton: Taylor & Francis Inc, 2013.
17. 羅美玲, 譚紅專, 周權, 等. 在R軟件中實現單個率的Meta分析. 循證醫學, 2013, 13(3): 181-184.
18. Tebbs JM. An introduction to categorical data analysis (2nd Edition). New York: Wiley, 1996.
19. 劉關鍵, 吳泰相. 循證醫學中統計指標的正確應用. 中國臨床康復, 2003, 7(3): 359-362, 365.
20. Newcombe RG. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med, 1998, 17(8): 857-872.
21. Cheng J, Pullenayegum E, Marshall JK, et al. Impact of including or excluding both-armed zero-event studies on using standard meta-analysis methods for rare event outcome: a simulation study. BMJ Open, 2016, 6(8): 1-10.
22. 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.
23. 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.
24. Steadman SS, Lisa AW, Michael JF, et al. An assessment of the use of the continuity correction for sparse data in meta-analysis. Commun Stat Simul Comput, 2014, 25(4): 1031-1056.
25. Efthimiou O. Practical guide to the meta-analysis of rare events. Evid Based Ment Health, 2018, 21(2): 72-76.

Chinese Journal of Evidence-Based Medicine

Meta-analysis of single rates with zero events

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