Quantitative Analysis of Bias of Each Study in Meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

 FUJin-yu ,  QINChao-ying

Applied Mathematics, School of Nature and Applied Sciences, Northwestern Polytechnical University, Xi'an 710129, China;

Corresponding author：

QINChao-ying, Email: qinchaoying@nwpu.edu.cn

Keywords：

Meta-analysis; Skew normal distribution; Maximum likelihood estimation; Bias; Heterogeneity; Combined effect size

DOI：

10.7507/1672-2531.20160169

Video：

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Objective Study how to quantify the bias of each study and how to estimate them. Method In the random-effect model, it is commonly assumed that the effect size of each study in meta-analysis follows a skew normal distribution which has different shape parameter. Through introducing a shape parameter to quantify the bias and making use of Markov estimation as well as maximum likelihood estimation to estimate the overall effect size, bias of each study, heterogeneity variance. Result In simulation study, the result was closer to the real value when the effect size followed a skew normal distribution with different shape parameter and the impact of heterogeneity of random effects meta-analysis model based on the skew normal distribution with different shape parameter was smaller than it in a random effects metaanalysis model. Moreover, in this specific example, the length of the 95%CI of the overall effect size was shorter compared with the model based on the normal distribution. Conclusion Incorporate the bias of each study into the random effects meta-analysis model and by quantifying the bias of each study we can eliminate the influence of heterogeneity caused by bias on the pooled estimate, which further make the pooled estimate closer to its true value.

Citation： FUJin-yu, QINChao-ying. Quantitative Analysis of Bias of Each Study in Meta-analysis. Chinese Journal of Evidence-Based Medicine, 2016, 16(9): 1112-1116. doi: 10.7507/1672-2531.20160169 Copy

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1. Ellenberg SS. Meta-analysis: the quantitive approach to research review. Seminats Oncol, 1998, 15(5): 427-481.
2. Biggerstaff BJ, Tweedie RL. Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Stat Med, 1997, 16(7): 753-768.
3. 党红, 秦超英, 刘金涛.基于偏正态分布的随机效应meta回归.纺织高校基础科学学报, 2013, 26(1): 106-109.
4. 党红, 秦超英, 刘金涛. Meta分析中基于偏正态分布的总体效应估计及其应用.纺织高校基础科学学报, 2014, 27(4): 459-462, 481.
5. 郑萍, 秦超英, 秦思达.基于效应量服从偏态分布的PM法.科学技术与工程, 2015, 15(31): 117-119, 125.
6. Azzalini A. A class of distributions which includes the normal ones. Scand J Statist, 1985, 12 (2): 171-178.
7. Azzalini A. Further results on a class of distributions which includes the normal ones. Statistical, 1986, 46(2): 199-208.
8. Henze N. A probabilistic representation on the ' skew-normal' distribution. Scand J Statist, 1986, 13(4): 271-275.
9. Hardy RJ, Thompson SG. A likelihood approach to meta-analysis with random effects. Stat Med, 1996, 15(96): 619-629.
10. 覃金春, 阳志军, 熊黎, 等.系统性腹膜后淋巴结清扫对上皮性卵巢癌患者预后影响的Meta分析.中国循证医学杂志, 2012, 12(2): 224-230.

Chinese Journal of Evidence-Based Medicine

Quantitative Analysis of Bias of Each Study in Meta-analysis

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