Multi-Levels Statistical Model in the Heterogeneity Control of Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

QIU Yaoqin ¹ ,  HE Jia ²

1. Department of Nursing, the Second Military Medical University, Shanghai 200433, China;2. Department of Health Statistics, the Second Military Medical University, Shanghai 200433, China;

Corresponding author：

HE Jia, Email: hejiaweixin@hotmail.com

Keywords：

Multi-levels statistical model; Meta-analysis; Heterogeneity; Control

DOI：

10.7507/1672-2531.20110124

Video：

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Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.

Citation： QIU Yaoqin,HE Jia. Multi-Levels Statistical Model in the Heterogeneity Control of Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2011, 11(6): 711-715. doi: 10.7507/1672-2531.20110124 Copy

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2.	5 Colditz GA, Burdick E, Moseller F. Heterogeneity in meta-analysis of data from epidemiologic studies: Commentary. Am J Epidemiol , 1995, 142: 371-382.
3.	7 Bashore TR, Osman A, Heley EF. Mental slowing in elderly persons: a cognitive psychophysiological analysis. Psychology Aging , 1989, 4(2): 235-244.
4.	8 Greenland S. A critical look at some popular meta-analytic methods. Am J Epidemiol , 1994, 140(3): 290-296.
5.	9 Turner RM, Omar RZ, Yang M, et al . A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med , 2000, 19(24): 3417-3432.
6.	10 Higgins JPT, Whitehead A, Turner RM, et al . Meta-analysis of continuous outcome data from individual patients. Stat Med , 2001, 20(15): 2219-2241.
7.	11 Thompson SG, Turner RM, Warn DE. Multilevel models for meta-analysis. Stat Methods Med Res , 2001, 10(6): 375.
8.	14 Lambert PC, Sutton AJ, Abrams KR, et al . A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. J Clin Epidemiol , 2002, 55(1): 86-94.
9.	15 Schwartz S. The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. Am J Public Health , 1994, 84(5): 819-824.
10.	16 Berlin JA, Santanna J, Schmid CH, et al , for the Anti-lymphocyte Antibody Induction Therapy Study Group. Individual patient versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med , 2002, 21(3): 371-387.
11.	17 Brand R, Kragt H. Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials. Stat Med , 1992, 11(16): 2077-2082.
12.	18 McIntosh M. The population risk as an explanatory variable in research synthesis of clinical trials. Stat Med , 1996, 15(16): 1713–1728.
13.	19 Thompson SG, Smith TC, Sharp SJ. Investigating underlying risk as a source of heterogeneity in meta-analysis. Stat Med , 1997, 16(23): 2741-2758.
14.	20 Lau J, Ioannidis JPA, Schmid CH. Summing up evidence: one answer is not always enough. Lancet , 1998, 351(9096): 123-127.
15.	21 Schmid CH, Lau J, McIntosh MW, et al . An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med , 1998, 17(17): 1923-1942.
16.	22 Arends LR, Hoes AW, Lubsen J, et al . Baseline risk as predictor of treatment benefit: three clinical meta-re-analyses. Stat Med , 2000, 19(24): 3497-3518.
17.	23 Rebecca M. Turner RM, Omar RZ, et al . Bayesian methods of analysis for cluster randomized trials with binary outcome data. Stat Med , 2001, 20(3): 453-472.
18.	24 Smith AFM, Roberts GO. Bayesian computation via the Gibbs sampler and related Maekov chain Monte Carlo methods. J Roy Stat Soc , Series B , 1993, 55(1): 3-23.
19.	Green S, Higgins J. Glossary. Cochrane handbook for systematic reviews of interventions 4.2.5. The Cochrane Collaboration [ 2009-05-19]. http://www.cochrane.org/resources/glossary.htm.
20.	Cochrane Handbook for Systematic Reviews of Interventions 4.2.6 Updated September 2006. The Cochrane Collaboration, 2006: l36.
21.	王建华. 流行病学. 第6版. 北京: 人民卫生出版社, 2004: 179.
22.	Goldstein H. Multilevel Statistical Models. 李晓松, 陈滔, 潘晓平, 译. 第2版. 成都: 四川科学技术出版社, 1999: 50.
23.	王安伟, 黄文丽. 多水平meta回归分析及其在流行病学研究中的应用. 大理学院学报, 2007, 6(12): 84.
24.	Kreft I, deLeeuw J. Introducing multilevel modeling. Thousand Oaks, CA: Sage; 1998: 41.
25.	张言彩. 结构方程模型的Gibbs抽样与贝叶斯估计. 统计与决策, 2009, (6): 23-25.
26.	周旭毓, 方积乾. Meta分析中随机效应模型的Gibbs抽样及其应用. 中国卫生统计, 2002, 1(4): 204-207.

1. 2 Engels EA, Schmid CH, Terrin N, et al . Heterogeneity and statistical significance in meta-analysis: an empirical study of 125 meta-analyses. Stat Med , 2000, 19(13): 1707-1728.
2. 5 Colditz GA, Burdick E, Moseller F. Heterogeneity in meta-analysis of data from epidemiologic studies: Commentary. Am J Epidemiol , 1995, 142: 371-382.
3. 7 Bashore TR, Osman A, Heley EF. Mental slowing in elderly persons: a cognitive psychophysiological analysis. Psychology Aging , 1989, 4(2): 235-244.
4. 8 Greenland S. A critical look at some popular meta-analytic methods. Am J Epidemiol , 1994, 140(3): 290-296.
5. 9 Turner RM, Omar RZ, Yang M, et al . A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med , 2000, 19(24): 3417-3432.
6. 10 Higgins JPT, Whitehead A, Turner RM, et al . Meta-analysis of continuous outcome data from individual patients. Stat Med , 2001, 20(15): 2219-2241.
7. 11 Thompson SG, Turner RM, Warn DE. Multilevel models for meta-analysis. Stat Methods Med Res , 2001, 10(6): 375.
8. 14 Lambert PC, Sutton AJ, Abrams KR, et al . A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. J Clin Epidemiol , 2002, 55(1): 86-94.
9. 15 Schwartz S. The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. Am J Public Health , 1994, 84(5): 819-824.
10. 16 Berlin JA, Santanna J, Schmid CH, et al , for the Anti-lymphocyte Antibody Induction Therapy Study Group. Individual patient versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med , 2002, 21(3): 371-387.
11. 17 Brand R, Kragt H. Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials. Stat Med , 1992, 11(16): 2077-2082.
12. 18 McIntosh M. The population risk as an explanatory variable in research synthesis of clinical trials. Stat Med , 1996, 15(16): 1713–1728.
13. 19 Thompson SG, Smith TC, Sharp SJ. Investigating underlying risk as a source of heterogeneity in meta-analysis. Stat Med , 1997, 16(23): 2741-2758.
14. 20 Lau J, Ioannidis JPA, Schmid CH. Summing up evidence: one answer is not always enough. Lancet , 1998, 351(9096): 123-127.
15. 21 Schmid CH, Lau J, McIntosh MW, et al . An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med , 1998, 17(17): 1923-1942.
16. 22 Arends LR, Hoes AW, Lubsen J, et al . Baseline risk as predictor of treatment benefit: three clinical meta-re-analyses. Stat Med , 2000, 19(24): 3497-3518.
17. 23 Rebecca M. Turner RM, Omar RZ, et al . Bayesian methods of analysis for cluster randomized trials with binary outcome data. Stat Med , 2001, 20(3): 453-472.
18. 24 Smith AFM, Roberts GO. Bayesian computation via the Gibbs sampler and related Maekov chain Monte Carlo methods. J Roy Stat Soc , Series B , 1993, 55(1): 3-23.
19. Green S, Higgins J. Glossary. Cochrane handbook for systematic reviews of interventions 4.2.5. The Cochrane Collaboration [ 2009-05-19]. http://www.cochrane.org/resources/glossary.htm.
20. Cochrane Handbook for Systematic Reviews of Interventions 4.2.6 Updated September 2006. The Cochrane Collaboration, 2006: l36.
21. 王建华. 流行病学. 第6版. 北京: 人民卫生出版社, 2004: 179.
22. Goldstein H. Multilevel Statistical Models. 李晓松, 陈滔, 潘晓平, 译. 第2版. 成都: 四川科学技术出版社, 1999: 50.
23. 王安伟, 黄文丽. 多水平meta回归分析及其在流行病学研究中的应用. 大理学院学报, 2007, 6(12): 84.
24. Kreft I, deLeeuw J. Introducing multilevel modeling. Thousand Oaks, CA: Sage; 1998: 41.
25. 张言彩. 结构方程模型的Gibbs抽样与贝叶斯估计. 统计与决策, 2009, (6): 23-25.
26. 周旭毓, 方积乾. Meta分析中随机效应模型的Gibbs抽样及其应用. 中国卫生统计, 2002, 1(4): 204-207.

Chinese Journal of Evidence-Based Medicine

Multi-Levels Statistical Model in the Heterogeneity Control of Meta-analysis

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