QE or RE? A bias adjusted weighting procedure for meta-analysis

Chang Xu; Xian Tao Zeng; Suhail Doi; Tong Zu Liu; Xin Yin Kuang; Hao Chen; Chao Zhang

doi:10.7507/1672-2531.20160094

QE or RE? A bias adjusted weighting procedure for meta-analysis

Chang Xu, Xian Tao Zeng, Suhail Doi, Tong Zu Liu, Xin Yin Kuang, Hao Chen, Chao Zhang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Article number	018
Pages (from-to)	612-616
Number of pages	5
Journal	Chinese Journal of Evidence-Based Medicine
Volume	16
Issue number	5
DOIs	https://doi.org/10.7507/1672-2531.20160094
Publication status	Published - 2016

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10.7507/1672-2531.20160094

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title = "QE or RE? A bias adjusted weighting procedure for meta-analysis",

abstract = "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.",

keywords = "Bias, Meta-analysis, Quality Effect, Random effect model",

author = "Chang Xu and Zeng, {Xian Tao} and Suhail Doi and Liu, {Tong Zu} and Kuang, {Xin Yin} and Hao Chen and Chao Zhang",

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T1 - QE or RE? A bias adjusted weighting procedure for meta-analysis

AU - Xu, Chang

AU - Zeng, Xian Tao

AU - Doi, Suhail

AU - Liu, Tong Zu

AU - Kuang, Xin Yin

AU - Chen, Hao

AU - Zhang, Chao

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

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QE or RE? A bias adjusted weighting procedure for meta-analysis

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