How to Perform Dose-response Meta-analysis: A Brief Introduction of Methodology_Chinese Journal of Evidence-Based Medicine

Authors：

 XUChang ^1,2 , ZHANGYong-gang ³ , HANFang-fang ⁴ , NIUYu-ming ^1,5 , KUANGXin-ying ³ ,  ZHANGChao ¹

1. Center for Evidence-based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China;
2. Department of Urology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China;
3. West China Hospital, Sichuan University, Chengdu 610041, China;
4. Department of Ophthalmology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China;
5. Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China;

Corresponding author：

ZHANGChao, Email: zhangchao0803@126.com

Keywords：

Dose-response relationship; Meta-analysis; Methodology

DOI：

10.7507/1672-2531.20150204

Video：

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Abstract Full text Figures/Tables Video References Cited by

Does-response meta-analysis, which has being developed for more than 30 years, is a type of regression function and can be both linear and non-linear model. It plays an important role in investigating the relationship between dependent and independent variable. With its special advantages, dose-response meta-analysis has been widely used in evidence-based practice and decision. Currently there are several models can be used to perform dose-response metaanalysis with various advantages and disadvantages. It is vital to choose best model to perform dose-response metaanalysis in evidence-based practice. In this paper, we briefly introduce and summarize the methodology of dose-response meta-analysis.

Citation： XUChang, ZHANGYong-gang, HANFang-fang, NIUYu-ming, KUANGXin-ying, ZHANGChao. How to Perform Dose-response Meta-analysis: A Brief Introduction of Methodology. Chinese Journal of Evidence-Based Medicine, 2015, 15(10): 1236-1239. doi: 10.7507/1672-2531.20150204 Copy

1.	徐畅, 曾宪涛, 张超, 等. 应用R软件dosresmeta程序包和mvmeta程序包实现剂量-反应关系Meta分析. 中国循证医学杂志, 2015, 15(4):479-483.
2.	Sauerbrei W, Royston P. A new strategy for meta-analysis of continuous covariates in observational studies. Stat Med, 2011, 30(28):3341-3360.
3.	Guyatt GH, Oxman AD, Vist GE, et al. GRADE:an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336(7650):924-946.
4.	徐畅, 刘同族, 邝心颖, 等. 剂量-反应Meta分析模型中缺失值的处理及效应指标的转换. 中国循证医学杂志, 2015, 15(8):984-987.
5.	曾宪涛, 主编. 应用STATA做Meta分析. 北京:军事医学科学出版社, 2014:92-100.
6.	Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol, 1999, 28(5):964-974.
7.	Royston P. A strategy for modelling eth effect of a continuous covariate in medicine and epidemiology. Stat Med, 2000, 19(14):1831-1847.
8.	Orsini N, Li R, Wolk A, et al. Meta-analysis for linear and nonlinear dose-response relations:examples, an evaluation of approximations, and software. Am J Epidemiol, 2012, 175(1):66-73.
9.	Gasparrini A, Armstrong B. Multivariate meta-analysis:a method to summarize non-linear associations. Stat Med, 2011, 30(20):2504-2506.
10.	Qin L, Cook NR, Bergstrom A, et al. A two-stage hierarchical regression model for meta-analysis of epidemiologic nonlinear dose-response data. CS & DA, 2009, 53(12):4157-4167.
11.	Mills PK, Beeson WL, Phillips RL, Fraser GE. Cohort study of diet, lifestyle, and prostate cancer in Adventist men. Cancer, 1989, 64(3):598-604.
12.	Xu C, Han FF, Zeng XT, et al. Fat intake is not linked to prostate cancer:a systematic review and dose-response meta-analysis. PLOS one, 2015, 10(7):e0131747.
13.	Maclure M, Greenland S. Tests for trend and dose response:misinterpretations and alternatives. Am J Epidemiol, 1992, 135(1):96-104.
14.	Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol, 1992, 135(11):1301-1309.
15.	孙振球. 医学统计学. 第三版. 北京:人民卫生出版社, 2002:149-155.
16.	Brigham JC, Aquino W, Aguilo MA, et al. A Spectral Finite Element Approach to Modeling Soft Solids Excited with High-Frequency Harmonic Loads. Comput Methods Appl Mech Eng, 2011, 200(5-8):692-698.
17.	同济大学数学系. 高等数学(上). 第七版. 北京:高等教育出版社, 2014:73-96.

1. 徐畅, 曾宪涛, 张超, 等. 应用R软件dosresmeta程序包和mvmeta程序包实现剂量-反应关系Meta分析. 中国循证医学杂志, 2015, 15(4):479-483.
2. Sauerbrei W, Royston P. A new strategy for meta-analysis of continuous covariates in observational studies. Stat Med, 2011, 30(28):3341-3360.
3. Guyatt GH, Oxman AD, Vist GE, et al. GRADE:an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336(7650):924-946.
4. 徐畅, 刘同族, 邝心颖, 等. 剂量-反应Meta分析模型中缺失值的处理及效应指标的转换. 中国循证医学杂志, 2015, 15(8):984-987.
5. 曾宪涛, 主编. 应用STATA做Meta分析. 北京:军事医学科学出版社, 2014:92-100.
6. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol, 1999, 28(5):964-974.
7. Royston P. A strategy for modelling eth effect of a continuous covariate in medicine and epidemiology. Stat Med, 2000, 19(14):1831-1847.
8. Orsini N, Li R, Wolk A, et al. Meta-analysis for linear and nonlinear dose-response relations:examples, an evaluation of approximations, and software. Am J Epidemiol, 2012, 175(1):66-73.
9. Gasparrini A, Armstrong B. Multivariate meta-analysis:a method to summarize non-linear associations. Stat Med, 2011, 30(20):2504-2506.
10. Qin L, Cook NR, Bergstrom A, et al. A two-stage hierarchical regression model for meta-analysis of epidemiologic nonlinear dose-response data. CS & DA, 2009, 53(12):4157-4167.
11. Mills PK, Beeson WL, Phillips RL, Fraser GE. Cohort study of diet, lifestyle, and prostate cancer in Adventist men. Cancer, 1989, 64(3):598-604.
12. Xu C, Han FF, Zeng XT, et al. Fat intake is not linked to prostate cancer:a systematic review and dose-response meta-analysis. PLOS one, 2015, 10(7):e0131747.
13. Maclure M, Greenland S. Tests for trend and dose response:misinterpretations and alternatives. Am J Epidemiol, 1992, 135(1):96-104.
14. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol, 1992, 135(11):1301-1309.
15. 孙振球. 医学统计学. 第三版. 北京:人民卫生出版社, 2002:149-155.
16. Brigham JC, Aquino W, Aguilo MA, et al. A Spectral Finite Element Approach to Modeling Soft Solids Excited with High-Frequency Harmonic Loads. Comput Methods Appl Mech Eng, 2011, 200(5-8):692-698.
17. 同济大学数学系. 高等数学(上). 第七版. 北京:高等教育出版社, 2014:73-96.

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