Meta-analysis of repeated measurement data based on mixed effects model of SAS software_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Ying ¹ , HUANG Bifen ² ,  ZHENG Jianqing ¹ , XIAO Lihua ¹ , WU Min ¹

1. Department of Radiation Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, P.R.China;
2. Department of Obstetrics and Gynecology, Quanzhou Medical College People's Hospital Affiliated, Quanzhou, 362000, P.R.China;

Corresponding author：

ZHENG Jianqing, Email: 18060108268@189.cn

Keywords：

Generalized linear mixed-effects model; Covariate analysis; Meta-analysis; SAS macro

DOI：

10.7507/1672-2531.201902004

Video：

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Despite the rapid development of meta-analysis technology, there were currently no consolidation technology for longitudinal data. The meta-analysis model based on the generalized linear mixed-effects model can fully encapsulate the correlation between various time points and accurately estimate the final combined effect, which is an ideal model for longitudinal-data meta-analysis. Through example data, this paper used SAS software to realize longitudinal-data meta-analysis and provided programming codes.

Citation： CHEN Ying, HUANG Bifen, ZHENG Jianqing, XIAO Lihua, WU Min. Meta-analysis of repeated measurement data based on mixed effects model of SAS software. Chinese Journal of Evidence-Based Medicine, 2019, 19(8): 998-1006. doi: 10.7507/1672-2531.201902004 Copy

1.	陈彦靓, 田茂再. 关于纵向数据分析方法的比较研究. 统计与决策, 2013, (10): 23-26.
2.	Peters JL, Mengersen KL. Meta-analysis of repeated measures study designs. J Eval Clin Pract, 2008, 14(5): 941-950.
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5.	张天嵩, 董圣杰, 周支瑞. 高级 Meta 分析方法—基于 Stata 实现. 上海: 复旦大学出版社, 2015.
6.	Mavridis D, Salanti G. A practical introduction to multivariate meta-analysis. Stat Methods Med Res, 2013, 22(2): 133-158.
7.	Riley RD. Multivariate meta-analysis: the effect of ignoring within-study correlation. J Roy Stat, 2009, 172(4): 789-811.
8.	Musekiwa A, Manda SO, Mwambi HG, et al. Meta-analysis of effect sizes reported at multiple time points using general linear mixed model. PLoS One, 2016, 11(10): e0164898.
9.	Trikalinos TA, Olkin I. Meta-analysis of effect sizes reported at multiple time points: a multivariate approach. Clin Trials, 2012, 9(5): 610-620.
10.	Jansen JP, Vieira MC, Cope S. Network meta-analysis of longitudinal data using fractional polynomials. Stat Med, 2015, 34(15): 2294-2311.
11.	Ishak KJ, Platt RW, Joseph L, et al. Impact of approximating or ignoring within-study covariances in multivariate meta-analyses. Stat Med, 2008, 27(5): 670-686.
12.	Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med, 2008, 27(22): 4381-4396.
13.	Riley RD, Abrams KR, Sutton AJ, et al. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Med Res Methodol, 2007, 7: 3.
14.	Jennrich RI, Schluchter MD. Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 1986, 42(4): 805-820.
15.	Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyze unbalanced repeated measures and longitudinal data. Stat Med, 1997, 16(20): 2349-2380.

1. 陈彦靓, 田茂再. 关于纵向数据分析方法的比较研究. 统计与决策, 2013, (10): 23-26.
2. Peters JL, Mengersen KL. Meta-analysis of repeated measures study designs. J Eval Clin Pract, 2008, 14(5): 941-950.
3. Senn S, Stevens L, Chaturvedi N. Repeated measures in clinical trials: simple strategies for analysis using summary measures. Stat Med, 2000, 19(6): 861-877.
4. Ishak KJ, Platt RW, Joseph L, et al. Meta-analysis of longitudinal studies. Clin Trials, 2007, 4(5): 525-539.
5. 张天嵩, 董圣杰, 周支瑞. 高级 Meta 分析方法—基于 Stata 实现. 上海: 复旦大学出版社, 2015.
6. Mavridis D, Salanti G. A practical introduction to multivariate meta-analysis. Stat Methods Med Res, 2013, 22(2): 133-158.
7. Riley RD. Multivariate meta-analysis: the effect of ignoring within-study correlation. J Roy Stat, 2009, 172(4): 789-811.
8. Musekiwa A, Manda SO, Mwambi HG, et al. Meta-analysis of effect sizes reported at multiple time points using general linear mixed model. PLoS One, 2016, 11(10): e0164898.
9. Trikalinos TA, Olkin I. Meta-analysis of effect sizes reported at multiple time points: a multivariate approach. Clin Trials, 2012, 9(5): 610-620.
10. Jansen JP, Vieira MC, Cope S. Network meta-analysis of longitudinal data using fractional polynomials. Stat Med, 2015, 34(15): 2294-2311.
11. Ishak KJ, Platt RW, Joseph L, et al. Impact of approximating or ignoring within-study covariances in multivariate meta-analyses. Stat Med, 2008, 27(5): 670-686.
12. Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med, 2008, 27(22): 4381-4396.
13. Riley RD, Abrams KR, Sutton AJ, et al. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Med Res Methodol, 2007, 7: 3.
14. Jennrich RI, Schluchter MD. Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 1986, 42(4): 805-820.
15. Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyze unbalanced repeated measures and longitudinal data. Stat Med, 1997, 16(20): 2349-2380.

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