Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models and they have some advantages over the widely used bias-correction method of the trim-and-fill method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals or require a complicated sensitivity analysis process. In this paper, we develop a simple publication bias adjustment method utilizing information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equation for parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under missing not at random. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I2 measure are proposed. We propose methods to construct asymptotic confidence intervals and suggest intervals based on parametric bootstrapping as an alternative. Through numerical experiments, we observed that the estimators successfully eliminate biases and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the asymptotic confidence interval is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use.
翻译:在进行系统审查和元分析方面,出版偏差是一个主要问题。根据选择模式,已经制定了各种敏感性分析或纠正偏差的方法,这些方法对广泛使用的三至填充方法的偏差校正方法有一些优势;然而,根据选择模式采用的可能性方法可能难以获得准确的估计数和合理的信任间隔,或需要复杂的敏感度分析过程。在本文件中,我们利用临床试验登记册已经进行但尚未公布的试验的资料,开发了简单的出版偏差调整方法。我们在选择功能中引入了参数估计方程式,将出版偏差问题作为一个缺失的数据问题作为缺失的数据问题,而不随机。在估计选择功能中,我们引入了反概率加权方法,以估计整个研究的总体平均值。此外,还提出了IPW的异质性计量方法,如研究差异和I2措施等,需要复杂的敏感度分析过程。我们提出一些方法,用以构建零度信任度间隔,并根据对准轨距的间隔作为替代方法。我们通过数字实验发现,测量者成功地消除偏差和信任度的间隔方法,作为更接近的间隔,因此,在近的间隔中,在实际的间隔期中建议采用。