多水平統計模型在Meta分析異質性控制中的作用_《中國循證醫學雜志》

作者：

仇瑤琴 ¹ ,  賀佳 ²

1. 第二軍醫大學護理系（上海?200433）；2. 第二軍醫大學衛生統計學教研室（上海?200433）;

關鍵詞：

多水平統計模型 Meta分析異質性控制

DOI：

10.7507/1672-2531.20110124

視頻：

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檢索并綜合國內外Meta分析異質性處理的相關文獻，介紹基于隨機效應的Meta回歸、基線風險效應模型及基于分層貝葉斯的隨機效應模型等多水平統計模型在Meta分析異質性控制中的應用。多水平統計模型將傳統模型中單一的隨機誤差項分解到與數據層次結構相對應的水平上，其擬合效應不僅能使Meta分析結果更為穩健與合理，而且能通過對協變量的解釋指導臨床具體問題。

引用本文： 仇瑤琴,賀佳. 多水平統計模型在Meta分析異質性控制中的作用. 中國循證醫學雜志, 2011, 11(6): 711-715. doi: 10.7507/1672-2531.20110124 復制

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.	王建華. 流行病學. 第6版. 北京: 人民衛生出版社, 2004: 179.
22.	Goldstein H. Multilevel Statistical Models. 李曉松, 陳滔, 潘曉平, 譯. 第2版. 成都: 四川科學技術出版社, 1999: 50.
23.	王安偉, 黃文麗. 多水平meta回歸分析及其在流行病學研究中的應用. 大理學院學報, 2007, 6(12): 84.
24.	Kreft I, deLeeuw J. Introducing multilevel modeling. Thousand Oaks, CA: Sage; 1998: 41.
25.	張言彩. 結構方程模型的Gibbs抽樣與貝葉斯估計. 統計與決策, 2009, (6): 23-25.
26.	周旭毓, 方積乾. Meta分析中隨機效應模型的Gibbs抽樣及其應用. 中國衛生統計, 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. 王建華. 流行病學. 第6版. 北京: 人民衛生出版社, 2004: 179.
22. Goldstein H. Multilevel Statistical Models. 李曉松, 陳滔, 潘曉平, 譯. 第2版. 成都: 四川科學技術出版社, 1999: 50.
23. 王安偉, 黃文麗. 多水平meta回歸分析及其在流行病學研究中的應用. 大理學院學報, 2007, 6(12): 84.
24. Kreft I, deLeeuw J. Introducing multilevel modeling. Thousand Oaks, CA: Sage; 1998: 41.
25. 張言彩. 結構方程模型的Gibbs抽樣與貝葉斯估計. 統計與決策, 2009, (6): 23-25.
26. 周旭毓, 方積乾. Meta分析中隨機效應模型的Gibbs抽樣及其應用. 中國衛生統計, 2002, 1(4): 204-207.

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多水平統計模型在Meta分析異質性控制中的作用

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多水平統計模型在Meta分析異質性控制中的作用

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