How to Estimate the Missing Data and Transform the Effect Measure in Dose-response Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

 XUChang ¹ , LIUTong-zu ¹ , KUANGXin-ying ² , ZHANGYong-gang ² , WENGHong ³ ,  ZHANGChao ⁴

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

Corresponding?author：

ZHANGChao, Email: zhangchao0803@126.com

Keywords：

Dose-response relationship; Meta-analysis; Missing data

DOI：

10.7507/1672-2531.20150164

Video：

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Dose-response relationship model has been widely used in epidemiology studies, as well as in evidence-based medicine area. In dose-response meta-analysis, the results are highly depended on the raw data. However, many primary studies did not provide sufficient data and led the difficulties in data analysis. The efficiency and response rate of collecting the raw data from original authors were always low, thus, evaluating and transforming the missing data is very important. In this paper, we summarized several types of missing data, and introduced how to estimate the missing data and transform the effect measure using the existed information.

Citation： XUChang, LIUTong-zu, KUANGXin-ying, ZHANGYong-gang, WENGHong, ZHANGChao. How to Estimate the Missing Data and Transform the Effect Measure in Dose-response Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2015, 15(8): 984-987. doi: 10.7507/1672-2531.20150164 Copy

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2.	徐暢, 曾憲濤, 張超, 等. 應用R軟件dosresmeta程序包和mvmeta程序包實現劑量-反應關系Meta分析. 中國循證醫學雜志, 2015, 15(4):479-483.
3.	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.
4.	曾憲濤, 主編. 應用 STATA做Meta分析. 北京:軍事醫學科學出版社, 2014:92-100.
5.	Xu C, Zeng XT, Liu TZ, et al. Fruits and Vegetables Intake and Risk of Bladder Cancer:A PRISMA-Compliant Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. Medicine (Baltimore), 2015; 94(17):e759.
6.	Easton DF, Peto J, Babiker AG. Floating absolute risk:an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat Med, 1991, 10(7):1025-1035.
7.	吳倩, 李衛, 王睿, 等. 浮動絕對危險:一種用于估計多水平影響因素可信區間的方法. 中華流行病學雜志, 2008, 29(12):1248-1250.
8.	Bekkering GE, Harris RJ, Thomas S, et al. How much of the data published in observational studies of the association between diet and prostate or bladder cancer is usable for meta-analysis? Am J Epidemiol, 2008; 167(9):1017-1026.
9.	Bagnardi V, Rota M, Botteri E, et al. Light alcohol drinking and cancer:a meta-analysis. Ann Oncol, 2013; 24(2):301-308.
10.	Aune D, Saugstad OD, Henriksen T, et al. Maternal body mass index and the risk of fetal death stillbirth, and infant death:a systematic review and meta-analysis. JAMA, 2014, 311(15):1536-1546.
11.	Orsini N. From floated to conventional confidence intervals for the relative risks based on published dose-response data. Comput Methods Programs Biomed, 2010, 98(1):90-93.
12.	Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA, 1998, 280(19):1690-1691.
13.	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.
14.	Xu C, Zhang C, Wang XL, et al. Self-Fluid Management in Prevention of Kidney Stones:APRISMA-Compliant Systematic Review and Dose-Response Meta-Analysis of Observational Studies. Medicine, 2015,94(27):e1042.

1. Berlin JA, Longnecker MP, Greenland S. Meta-analysis of epidemiologic dose-response data. Epidemiology, 1993, 4(3):218-228.
2. 徐暢, 曾憲濤, 張超, 等. 應用R軟件dosresmeta程序包和mvmeta程序包實現劑量-反應關系Meta分析. 中國循證醫學雜志, 2015, 15(4):479-483.
3. 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.
4. 曾憲濤, 主編. 應用 STATA做Meta分析. 北京:軍事醫學科學出版社, 2014:92-100.
5. Xu C, Zeng XT, Liu TZ, et al. Fruits and Vegetables Intake and Risk of Bladder Cancer:A PRISMA-Compliant Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. Medicine (Baltimore), 2015; 94(17):e759.
6. Easton DF, Peto J, Babiker AG. Floating absolute risk:an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat Med, 1991, 10(7):1025-1035.
7. 吳倩, 李衛, 王睿, 等. 浮動絕對危險:一種用于估計多水平影響因素可信區間的方法. 中華流行病學雜志, 2008, 29(12):1248-1250.
8. Bekkering GE, Harris RJ, Thomas S, et al. How much of the data published in observational studies of the association between diet and prostate or bladder cancer is usable for meta-analysis? Am J Epidemiol, 2008; 167(9):1017-1026.
9. Bagnardi V, Rota M, Botteri E, et al. Light alcohol drinking and cancer:a meta-analysis. Ann Oncol, 2013; 24(2):301-308.
10. Aune D, Saugstad OD, Henriksen T, et al. Maternal body mass index and the risk of fetal death stillbirth, and infant death:a systematic review and meta-analysis. JAMA, 2014, 311(15):1536-1546.
11. Orsini N. From floated to conventional confidence intervals for the relative risks based on published dose-response data. Comput Methods Programs Biomed, 2010, 98(1):90-93.
12. Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA, 1998, 280(19):1690-1691.
13. 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.
14. Xu C, Zhang C, Wang XL, et al. Self-Fluid Management in Prevention of Kidney Stones:APRISMA-Compliant Systematic Review and Dose-Response Meta-Analysis of Observational Studies. Medicine, 2015,94(27):e1042.

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How to Estimate the Missing Data and Transform the Effect Measure in Dose-response Meta-analysis

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