QE or RE? A Bias Adjusted Weighting Procedure for Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

 XUChang ^1,2 ^# ,  ZENGXian-Tao ^1,2 ^# , SuhailSuhail ³ , LIUTong-Zu ¹ , KUANGXin-Yin ⁴ , CHENHao ⁵ ,  ZHANGChao ^1,2,6

1. Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China;
2. Center of Evidence-based and translation medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China;
3. Research School of Population Health, Australian National University, Canberra, Australia;
4. Clinical Research and Evaluation Unit, Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041;
5. Department of Evidence-Based Medicine Center, Nanjing University of Chinese Medicine, Nanjing 210000, China;
6. 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：

Quality Effect; Random effect model; Meta-analysis; Bias

DOI：

10.7507/1672-2531.20160094

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

One important problem in meta-analysis is heterogeneity, the result of bias. When inconsistency occurs, traditional work in meta-analysis is employing a random effect model based on inverse variance method to combine the results. Such a method used the moment-based estimator τ² measuring the deviation from true value across studies to obtain a conservative result. It however failed to estimate the influence on each study due to bias and this method may at risk of underestimate the standard error which then may leads to biased summarized estimator. Accordingly, Doi proposed a new weighting procedure, QE method, hopefully be a good solution. In this article, we will introduce the QE method with details on the methodology and software, and then make a comparison between QE and random effect model of the results.

Citation： XUChang, ZENGXian-Tao, SuhailSuhail, LIUTong-Zu, KUANGXin-Yin, CHENHao, ZHANGChao. QE or RE? A Bias Adjusted Weighting Procedure for Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2016, 16(5): 612-616. doi: 10.7507/1672-2531.20160094 Copy

1.	Cochrane Collaboration. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [Updated March 2011]. Available at: http://handbook.cochrane.org/.
2.	Turner RM, Spiegelhalter DJ, Smith GC, et al. Bias modelling in evidence synthesis. Journal of the Royal Statistical Society. Series A, Statistics in Society, 2009, 172(1): 21-47.
3.	Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ, 2003, 327(7414): 557-560.
4.	Doi SA, Thalib L. A quality-effects model for meta-analysis. Epidemiology, 2008, 19(1): 94-100.
5.	Al Khalaf MM, Thalib L, Doi SA. Combining heterogenous studies using the random-effects model is a mistake and leads to inconclusive meta-analyses. J Clin Epidemiol, 2011, 64(2): 119-123.
6.	Doi SA, Barendregt JJ, Onitilo AA. Methods for the bias adjustment of meta-analyses of published observational studies. J Eval Clin Pract, 2013, 19(4): 653-657.
7.	Brockwell SE, Gordon IR. A comparison of statistical methods for meta-analysis. Stat Med, 2001, 20(6): 825-840.
8.	Doi SA, Barendregt JJ, Khan S, et al. Advances in the meta-analysis of heterogeneous clinical trials II: The quality effects model. Contemp Clin Trials, 2015, 45(Pt A): 123-129.
9.	Doi SA, Barendregt JJ, Khan S, et al. Simulation Comparison of the Quality Effects and Random Effects Methods of Meta-analysis. Epidemiology, 2015, 26(4): e42-44.
10.	盛骤, 谢式千, 潘承毅, 编著. 概率论与数理统计. 第四版. 北京: 高等教育出版社, 2010: 149-177.
11.	Doi SA, Barendregt JJ, Khan S, et al. Advances in the Meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model. Contemp Clin Trials, 2015, 45(Pt A): 130-138.
12.	Barendregt JJ, Doi SA. MetaXL User Guide (Verson 4.01). Available at: http://www.epigear.com/.
13.	Liu TZ, Xu C, Matteo R, et al. Sleep duration and risk of all-cause mortality: A flexible non-linear meta-regression of 40 prospective cohort studies. Sleep Med Rev, 2016, pii: S1087-0792(16)00021-6.

1. Cochrane Collaboration. Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [Updated March 2011]. Available at: http://handbook.cochrane.org/.
2. Turner RM, Spiegelhalter DJ, Smith GC, et al. Bias modelling in evidence synthesis. Journal of the Royal Statistical Society. Series A, Statistics in Society, 2009, 172(1): 21-47.
3. Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ, 2003, 327(7414): 557-560.
4. Doi SA, Thalib L. A quality-effects model for meta-analysis. Epidemiology, 2008, 19(1): 94-100.
5. Al Khalaf MM, Thalib L, Doi SA. Combining heterogenous studies using the random-effects model is a mistake and leads to inconclusive meta-analyses. J Clin Epidemiol, 2011, 64(2): 119-123.
6. Doi SA, Barendregt JJ, Onitilo AA. Methods for the bias adjustment of meta-analyses of published observational studies. J Eval Clin Pract, 2013, 19(4): 653-657.
7. Brockwell SE, Gordon IR. A comparison of statistical methods for meta-analysis. Stat Med, 2001, 20(6): 825-840.
8. Doi SA, Barendregt JJ, Khan S, et al. Advances in the meta-analysis of heterogeneous clinical trials II: The quality effects model. Contemp Clin Trials, 2015, 45(Pt A): 123-129.
9. Doi SA, Barendregt JJ, Khan S, et al. Simulation Comparison of the Quality Effects and Random Effects Methods of Meta-analysis. Epidemiology, 2015, 26(4): e42-44.
10. 盛骤, 谢式千, 潘承毅, 编著. 概率论与数理统计. 第四版. 北京: 高等教育出版社, 2010: 149-177.
11. Doi SA, Barendregt JJ, Khan S, et al. Advances in the Meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model. Contemp Clin Trials, 2015, 45(Pt A): 130-138.
12. Barendregt JJ, Doi SA. MetaXL User Guide (Verson 4.01). Available at: http://www.epigear.com/.
13. Liu TZ, Xu C, Matteo R, et al. Sleep duration and risk of all-cause mortality: A flexible non-linear meta-regression of 40 prospective cohort studies. Sleep Med Rev, 2016, pii: S1087-0792(16)00021-6.

Previous Article
Application of the Trial Sequential Analysis Software for Meta-analysis
Next Article
Guideline Protocol of Non-Pharmacological Secondary Prevention for Myocardial Infarction

Chinese Journal of Evidence-Based Medicine

QE or RE? A Bias Adjusted Weighting Procedure for Meta-analysis

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content