Psychological Sciences, School of Social Sciences, Humanities and Arts, The University of California, Merced.
Psychol Methods. 2015 Sep;20(3):310-30. doi: 10.1037/met0000046.
Researchers frequently conceptualize publication bias as a bias against publishing nonsignificant results. However, other factors beyond significance levels can contribute to publication bias. Some of these factors include study characteristics, such as the source of funding for the research project, whether the project was single center or multicenter, and prevailing theories at the time of publication. This article examines the relationship between publication bias and 2 study characteristics by breaking down 2 meta-analytic data sets into levels of the relevant study characteristic and assessing publication bias in each level with funnel plots, trim and fill (Duval & Tweedie, 2000a, 2000b), Egger's linear regression (Egger, Smith, Schneider, & Minder, 1997), cumulative meta-analysis (Borenstein, Hedges, Higgins, & Rothstein, 2009), and the Vevea and Hedges (1995) weight-function model. Using the Vevea and Hedges model, we conducted likelihood ratio tests to determine whether information was lost if only 1 pattern of selection was estimated. Results indicate that publication bias can differ over levels of study characteristics, and that developing a model to accommodate this relationship could be advantageous.
研究人员经常将发表偏倚概念化为对发表无显著性结果的偏见。然而,除了显著性水平之外,其他因素也可能导致发表偏倚。这些因素包括研究特征,例如研究项目的资金来源、研究是单中心还是多中心、以及发表时的流行理论。本文通过将两个荟萃分析数据集分解为相关研究特征的水平,并使用漏斗图、修剪和填充(Duval 和 Tweedie,2000a,2000b)、Egger 的线性回归(Egger、Smith、Schneider 和 Minder,1997)、累积荟萃分析(Borenstein、Hedges、Higgins 和 Rothstein,2009)和 Vevea 和 Hedges(1995)权重函数模型,评估每个水平的发表偏倚,来研究发表偏倚与两个研究特征之间的关系。使用 Vevea 和 Hedges 模型,我们进行似然比检验,以确定如果只估计一种选择模式,是否会丢失信息。结果表明,发表偏倚可能因研究特征的水平而异,并且开发一种能够适应这种关系的模型可能是有利的。