循证视角下的偏倚识别：基于<bold>Egger</bold>拓展模型的大数据元分析

doi:10.3772/j.issn.1000-0135.2024.04.009

情报学报

2024, Vol. 43

Issue (4): 491-502 DOI: 10.3772/j.issn.1000-0135.2024.04.009

情报技术与应用

本期目录 | 过刊浏览 | 高级检索

循证视角下的偏倚识别：基于Egger拓展模型的大数据元分析

周文杰^1,2,5, 林伟杰³, 魏志鹏^4,5, 杨克虎^4,5

1.中国人民大学信息资源管理学院，北京 100080
2.西北师范大学商学院，兰州 730070
3.北京交通大学经济管理学院，北京 100044
4.兰州大学基础医学院循证医学中心，兰州 730030
5.兰州大学循证社会科学交叉创新实验室，兰州 730030

Bias Identification from an Evidence-Based Perspective: A Big Data Meta-Analysis Procedure Based on Egger's Extension Model

Zhou Wenjie^1,2,5, Lin Weijie³, Wei Zhipeng^4,5, Yang Kehu^4,5

1.School of Information Resource Management, Renmin University of China, Beijing 100080
2.Business School of Northwest Normal University, Lanzhou 730070
3.School of Economics and Management, Beijing Jiaotong University, Beijing 100044
4.Evidence-Based Medical Center of School of Basic Medical Sciences of Lanzhou University, Lanzhou 730030
5.Cross-Innovation Laboratory of Evidence-Based Social Science of Lanzhou University, Lanzhou 730030

摘要
图/表
参考文献
相关文章 (2)

全文: PDF (780 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要证据综合是实现学术研究发现向实践指南制定转化的桥梁，元分析作为证据整合与转化工具，是循证体系建设的核心。然而，由于偏倚的存在，元分析结果的可靠性难以保障。针对循证研究过程中普遍存在的选择偏移和结果报告偏倚，本研究旨在围绕Egger等发展的模型，通过元回归对其加以拓展，并通过数学分解的方法对选择偏移和结果报告偏倚加以有效识别，从而发展出一种用于识别偏倚的新方法。在建立了精确的偏倚识别拓展模型的基础上，本研究使用一组经验研究数据，验证了拓展模型的合理性与科学性。本研究提出的拓展模型有效提高了Egger检验的效率，有助于提升元分析质量、构建和完善科学化的循证社会科学基础理论体系。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	周文杰
	林伟杰
	魏志鹏
	杨克虎

关键词 ：偏倚识别, Egger拓展模型, 元分析, 证据综合

收稿日期: 2023-07-10

基金资助:国家社会科学基金重大项目“循证社会科学的理论体系、国际经验与中国路径研究”（19ZDA142）。

作者简介: 周文杰，男，1973年生，教授，博士生导师，主要研究方向为循证社会科学，E-mail：wj_lp@sina.com；林伟杰，男，1996年生，博士研究生，主要研究方向为循证社会科学；魏志鹏，男，1982年生，博士研究生，主要研究方向为循证社会科学；杨克虎，男，1957年生，教授，博士生导师，主要研究方向为循证社会科学；

引用本文:

周文杰, 林伟杰, 魏志鹏, 杨克虎. 循证视角下的偏倚识别：基于Egger拓展模型的大数据元分析[J]. 情报学报, 2024, 43(4): 491-502.
Zhou Wenjie, Lin Weijie, Wei Zhipeng, Yang Kehu. Bias Identification from an Evidence-Based Perspective: A Big Data Meta-Analysis Procedure Based on Egger's Extension Model. 情报学报, 2024, 43(4): 491-502.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2024.04.009 或 https://qbxb.istic.ac.cn/CN/Y2024/V43/I4/491

1 White H. The twenty-first century experimenting society: the four waves of the evidence revolution[J]. Palgrave Communications, 2019, 5: Article No.47.
2 Hutton J L, Williamson P R. Bias in meta-analysis due to outcome variable selection within studies[J]. Journal of the Royal Statistical Society Series C: Applied Statistics, 2000, 49(3): 359-370.
3 Kirkham J J, Dwan K M, Altman D G, et al. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews[J]. British Medical Journal, 2010, 340(7747): 637-640.
4 Cooper H, Hedges L V, Valentine J C. The handbook of research synthesis and meta-analysis[M]. New York: Russell Sage Foundation, 2019: 384.
5 Egger M, Smith G D, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test[J]. British Medical Journal, 1997, 315(7109): 629-634.
6 Begg C B, Mazumdar M. Operating characteristics of a rank correlation test for publication bias[J]. Biometrics, 1994, 50(4): 1088-1101.
7 Duval S, Tweedie R. A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis[J]. Journal of the American Statistical Association, 2000, 95(449): 89-98.
8 Duval S, Tweedie R. Trim and fill: a simple funnel-plot - based method of testing and adjusting for publication bias in meta-analysis[J]. Biometrics, 2000, 56(2): 455-463.
9 Stanley T D, Doucouliagos H. Meta-regression analysis in economics and business[M]. New York: Routledge, 2012.
10 Vevea J L, Hedges L V. A general linear model for estimating effect size in the presence of publication bias[J]. Psychometrika, 1995, 60(3): 419-435.
11 Rücker G, Schwarzer G, Carpenter J R, et al. Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis[J]. Biostatistics, 2011, 12(1): 122-142.
12 Ulrich R, Miller J. p-hacking by post hoc selection with multiple opportunities: detectability by skewness test? Comment on Simonsohn, Nelson, and Simmons (2014)[J]. Journal of Experimental Psychology: General, 2015, 144(6): 1137-1145.
13 van Aert R C M, Wicherts J M, van Assen M A L M. Conducting meta-analyses based on p values[J]. Perspectives on Psychological Science, 2016, 11(5): 713-729.
14 Ioannidis J P A, Trikalinos T A. An exploratory test for an excess of significant findings[J]. Clinical Trials, 2007, 4(3): 245-253.
15 魏志鹏, 林伟杰, 杨克虎, 等. 循证视角下文献证据检索的内容效度研究[J]. 图书馆建设, 2023(1): 44-52.
16 Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis[J]. Nature, 2018, 555(7695): 175-182.
17 Rothman K J, Greenland S, Lash T L. Modern epidemiology[M]. 2nd edition. Philadelphia: Lippincott Williams and Wilkins, 1998.
18 Grimes D A, Schulz K F. Bias and causal associations in observational research[J]. The Lancet, 2002, 359(9302): 248-252.
19 Feinstein A R. Clinical epidemiology: the architecture of clinical research[M]. Philadelphia: W.B. Saunders Company, 1985.
20 Feinstein A R, Horwitz R I. Oestrogen treatment and endometrial carcinoma[J]. British Medical Journal, 1977, 2(6089): 766-767.
21 Seltzer C C, Bosse R, Garvey A J. Mail survey response by smoking status[J]. American Journal of Epidemiology, 1974, 100(6): 453-457.
22 Heckman J J. Sample selection bias as a specification error[J]. Econometrica, 1979, 47(1): 153-161.
23 Dwan K, Altman D G, Arnaiz J A, et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias[J]. PLoS One, 2008, 3(8): e3081.
24 Dickersin K, Min Y I, Meinert C L. Factors influencing publication of research results: follow-up of applications submitted to two institutional review boards[J]. Journal of the American Medical Association, 1992, 267(3): 374-378.
25 Song F J, Eastwood A, Gilbody S, et al. Publication and other selection biases in systematic reviews[J]. Health Technology Assessment, 2000, 4(10): 1-115.
26 Rothstein H R, Sutton A J, Borenstein M. Publication bias in meta-analysis: prevention, assessment and adjustments[M]. Chichester: John Wiley & Sons, 2005: 9-33.
27 Begg C B, Berlin J A. Publication bias: a problem in interpreting medical data[J]. Journal of the Royal Statistical Society Series A: Statistics in Society, 1988, 151(3): 419-463.
28 Barnes D E, Bero L A. Why review articles on the health effects of passive smoking reach different conclusions[J]. Journal of the American Medical Association, 1998, 279(19): 1566-1570.
29 Kepes S, Banks G C, McDaniel M, et al. Publication bias in the organizational sciences[J]. Organizational Research Methods, 2012, 15(4): 624-662.
30 Light R J, Pillemer D B. Summing up: the science of reviewing research[M]. Cambridge: Harvard University Press, 1984.
31 Kepes S, Bennett A A, McDaniel M A. Evidence-based management and the trustworthiness of our cumulative scientific knowledge: implications for teaching, research, and practice[J]. Academy of Management Learning & Education, 2014, 13(3): 446-466.
32 周文杰, 赵悦言, 魏志鹏, 等. 基于文献证据检索的循证研究效度检验机理探析[J]. 图书馆建设, 2023(1): 25-33, 43.
33 周文杰, 赵悦言, 魏志鹏, 等. 循证视角下文献证据检索的科学性评价: 缘起、指标与趋势[J]. 图书与情报, 2021(6): 31-36.
34 Reis H T, Judd C M. Handbook of research methods in social and personality psychology[M]. 2nd edition. New York: Cambridge University Press, 2014.
35 Petticrew M, Roberts H. Systematic reviews in the social sciences: a practical guide[M]. Oxford: Blackwell Publishing, 2006: 127.
36 StataCorp. Stata meta-analysis reference manual release 16[M]. Texas: Stata Press, 2019.
37 杨克虎, 李秀霞, 拜争刚. 循证社会科学研究方法: 系统评价与Meta分析[M]. 兰州: 兰州大学出版社, 2018.
38 于良芝, 周文杰. 信息穷人与信息富人: 个人层次的信息不平等测度述评[J]. 图书与情报, 2015(1): 53-60, 76.
39 于良芝. “个人信息世界”——一个信息不平等概念的发现及阐释[J]. 中国图书馆学报, 2013, 39(1): 4-12.
40 周文杰. 基于个人信息世界的信息分化研究[D]. 天津: 南开大学, 2013.
41 Vuong Q H. Likelihood ratio tests for model selection and non-nested hypotheses[J]. Econometrica, 1989, 57(2): 307-333.