Model Selection and Statistical Process of Meta-analysis of Dose-response Data_Chinese Journal of Evidence-Based Medicine

Authors：

 HUANGYu-bei ¹ , LIWei-qin ² , XIBo ³ , RONGYing ⁴ ,  SONGFeng-ju ¹ , CHENKe-xin ¹

1. Department of Epidemiology and Biostatistics, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China;
2. Tianjin Women's and Children's Health Center, Tianjin 3zgxzyxzz-16-2-223, China;
3. Department of Epidemiology, School of Public Health, Shandong University, Jinan 250012, China;
4. Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;

Corresponding author：

SONGFeng-ju, Email: songfengju@163.com

Keywords：

Dose-response relationship; Meta-analysis; GLST; Standardized process

DOI：

10.7507/1672-2531.20160035

Video：

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Abstract Full text Figures/Tables Video References Cited by

According to the heterogeneity between dose-response data across different studies and the potential nonlinear trend within the dose-response relationship, there are several models for trend estimation from summarized dose-response data, with applications to meta-analysis. However, up to now, there is no guideline of conducting a metaanalysis of dose-response data. After summarizing the previous papers, this paper focuses on how to select the right model for conducting a meta-analysis of dose-response data based on the heterogeneity across different studies, the goodness of fit, and the P value of overall association between exposure and event. Then a preliminary statistical process of conducting a meta-analysis of dose-response data is proposed.

Citation： HUANGYu-bei, LIWei-qin, XIBo, RONGYing, SONGFeng-ju, CHENKe-xin. Model Selection and Statistical Process of Meta-analysis of Dose-response Data. Chinese Journal of Evidence-Based Medicine, 2016, 16(2): 223-228. doi: 10.7507/1672-2531.20160035 Copy

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7.	Zhao W, Katzmarzyk PT, Horswell R, et al. Blood pressure and heart failure risk among diabetic patients. Int J Cardiol, 2014, 176(1) :125-132.
8.	Li W, Katzmarzyk PT, Horswell R, et al. Body mass index and heart failure among patients with type 2 diabetes mellitus. Circ Heart Fail, 2015, 8(3) :455-463.
9.	Bellavia A, Larsson SC, Bottai M, et al. Differences in survival associated with processed and with nonprocessed red meat consumption. Am J Clin Nutr, 2014, 100(3) :924-929.
10.	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.
11.	Orsini N, Bellocco R, Greenland S. Generalized least squares for trend estimation of summarized dose-response data. The Stata Journal, 2006, 6(1) :40-57.
12.	Zhou Y, Tian C, Jia C. Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes:a meta-analysis of prospective studies. Br J Nutr, 2012, 108(3) :408-417.
13.	Crippa A, Discacciati A, Larsson SC, et al. Coffee consumption and mortality from all causes, cardiovascular disease, and cancer:a doseresponse meta-analysis. Am J Epidemiol, 2014, 180(8) :63-775.
14.	Li S, Miao S, Huang Y, et al. Fruit intake decreases risk of incident type 2 diabetes:an updated meta-analysis. Endocrine, 2015, 48(2) : 454-460.
15.	Xi B, Huang Y, Reilly KH, et al. Sugar-sweetened beverages and risk of hypertension and CVD:a dose-response meta-analysis. Br J Nutr, 2015, 113(5) :709-717.
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1. Thacker SB. Meta-analysis. A quantitative approach to research integration. JAMA, 1988, 259(11) :1685-1689.
2. Greenland S. Quantitative methods in the review of epidemiologic literature. Epidemiol Rev, 1987, 9:1-30.
3. Dersimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials, 1986, 7(3) :177-188.
4. 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.
5. Berlin JA, Longnecker MP, Greenland S. Meta-analysis of epidemiologic dose-response data. Epidemiology, 1993, 4(3) :218-228.
6. Zheng W, Mclerran DF, Rolland B, et al. Association between bodymass index and risk of death in more than 1 million Asians. N Engl J Med, 2011, 364(8) :719-729.
7. Zhao W, Katzmarzyk PT, Horswell R, et al. Blood pressure and heart failure risk among diabetic patients. Int J Cardiol, 2014, 176(1) :125-132.
8. Li W, Katzmarzyk PT, Horswell R, et al. Body mass index and heart failure among patients with type 2 diabetes mellitus. Circ Heart Fail, 2015, 8(3) :455-463.
9. Bellavia A, Larsson SC, Bottai M, et al. Differences in survival associated with processed and with nonprocessed red meat consumption. Am J Clin Nutr, 2014, 100(3) :924-929.
10. 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.
11. Orsini N, Bellocco R, Greenland S. Generalized least squares for trend estimation of summarized dose-response data. The Stata Journal, 2006, 6(1) :40-57.
12. Zhou Y, Tian C, Jia C. Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes:a meta-analysis of prospective studies. Br J Nutr, 2012, 108(3) :408-417.
13. Crippa A, Discacciati A, Larsson SC, et al. Coffee consumption and mortality from all causes, cardiovascular disease, and cancer:a doseresponse meta-analysis. Am J Epidemiol, 2014, 180(8) :63-775.
14. Li S, Miao S, Huang Y, et al. Fruit intake decreases risk of incident type 2 diabetes:an updated meta-analysis. Endocrine, 2015, 48(2) : 454-460.
15. Xi B, Huang Y, Reilly KH, et al. Sugar-sweetened beverages and risk of hypertension and CVD:a dose-response meta-analysis. Br J Nutr, 2015, 113(5) :709-717.
16. Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med, 1989, 8(5) :551-561.

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