Comparison study of estimators of between-trial variance in trial sequential analysis for random-effects model_Chinese Journal of Evidence-Based Medicine

Authors：

WENG Hong ¹ , CAI Lin ² , WU Kaijie ³ , FANG Cheng ¹ , ZENG Guang ⁴ ,  ZENG Xiantao ^1,4

1. Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University; Center for Evidence-Based and Translational Medicine, Wuhan University; Department of Evidence-Based Medicine and Clinical Epidemiology, The Second Clinical Medical College of Wuhan University, Wuhan, 430071, P.R.China;
2. Department of Urology, Peking University First Hospital, Beijing, 100034, P.R.China;
3. Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P.R.China;
4. Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, P.R.China;

Corresponding author：

ZENG Xiantao, Email: zengxiantao1128@163.com

Keywords：

Trial sequential analysis; Meta-analysis; Random-effects model; Between-trial variance; Heterogeneity

DOI：

10.7507/1672-2531.201607063

Video：

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Abstract Full text Figures/Tables Video References Cited by

The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.

Citation： WENG Hong, CAI Lin, WU Kaijie, FANG Cheng, ZENG Guang, ZENG Xiantao. Comparison study of estimators of between-trial variance in trial sequential analysis for random-effects model. Chinese Journal of Evidence-Based Medicine, 2017, 17(3): 369-372. doi: 10.7507/1672-2531.201607063 Copy

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7.	Brockwell SE, Gordon IR. A comparison of statistical methods for meta-analysis. Stat Med, 2001, 20(6): 825-840.
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9.	Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educa Behav Stat, 2005, 30(3): 261-293.
10.	Inthout J, Ioannidis JP, 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.
11.	Brockwell SE, Gordon IR. A simple method for inference on an overall effect in meta-analysis. Stat Med, 2007, 26(25): 4531-4543.
12.	Sidik K, Jonkman J. Robust variance estimation for random-effects meta-analysis. Comp Stat Data An, 2006, 50(12): 3681-3701.
13.	Sánchez-Meca J, Marín-Martínez F. Confidence intervals for the overall effect size in random-effects meta-analysis. Psychol Methods, 2008, 13(1): 31-48.
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1. 翁鸿, 武珊珊, 张永刚, 等. 序贯 Meta 分析方法学简介. 中国循证医学杂志, 2016, 16(10): 1216-1220.
2. Thorlund K, Engstrøm J, Wetterslev J,et al. User manual for trial sequential analysis (TSA). Copenhagen Trial Unit, Centre for Clinical Intervention Research, Copenhagen, Denmark. 2011. Available at: www.ctu.dk/tsa.
3. 曾宪涛, Joey S.W. Kwong, 田国祥, 等. Meta 分析系列之二: Meta 分析的软件. 中国循证心血管医学杂志, 2012, 4(2): 89-91.
4. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials, 1986, 7(3): 177-188.
5. Sidik K, Jonkman JN. Simple heterogeneity variance estimation for meta-analysis. J R Stat Soc, 2005, 54(2): 367-384.
6. Sidik K, Jonkman JN. A comparison of heterogeneity variance estimators in combining results of studies. Stat Med, 2007, 26(9): 1964-1981.
7. Brockwell SE, Gordon IR. A comparison of statistical methods for meta-analysis. Stat Med, 2001, 20(6): 825-840.
8. Makambi KH. The effect of the heterogeneity variance estimator on some tests of treatment efficacy. J Biopharm Stat, 2004, 14(2): 439-449.
9. Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educa Behav Stat, 2005, 30(3): 261-293.
10. Inthout J, Ioannidis JP, 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.
11. Brockwell SE, Gordon IR. A simple method for inference on an overall effect in meta-analysis. Stat Med, 2007, 26(25): 4531-4543.
12. Sidik K, Jonkman J. Robust variance estimation for random-effects meta-analysis. Comp Stat Data An, 2006, 50(12): 3681-3701.
13. Sánchez-Meca J, Marín-Martínez F. Confidence intervals for the overall effect size in random-effects meta-analysis. Psychol Methods, 2008, 13(1): 31-48.
14. Biggerstaff BJ, Tweedie RL. Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Stat Med, 1997, 16(7): 753-768.

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