Department of Biomedical Statistics, Graduate School of Medicine, Osaka University, Osaka, Japan.
Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.
Biometrics. 2023 Sep;79(3):2089-2102. doi: 10.1111/biom.13822. Epub 2023 Jan 17.
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 trim-and-fill bias-correction method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals, or require a rather complicated sensitivity analysis process. Herein, we develop a simple publication bias adjustment method by utilizing the information on conducted but still unpublished trials from clinical trial registries.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 the missing not at random assumption. 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 I measure are proposed. We propose methods to construct confidence intervals based on asymptotic normal approximation as well as on parametric bootstrap. Through numerical experiments, we observed that the estimators successfully eliminated bias, and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the confidence interval based on asymptotic normal approximation is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use.
发表偏倚是进行系统评价和荟萃分析的主要关注点。已经基于选择模型开发了各种敏感性分析或偏差校正方法，它们相对于广泛使用的修剪和填充偏差校正方法具有一些优势。然而，基于选择模型的似然方法可能难以获得精确的估计值和合理的置信区间，或者需要相当复杂的敏感性分析过程。在此，我们开发了一种简单的发表偏倚调整方法，利用临床试验注册机构中已进行但尚未发表的试验信息。我们通过将发表偏倚问题视为缺失数据问题（在非随机缺失假设下），引入了一个用于选择函数参数估计的估计方程。通过估计的选择函数，我们引入逆概率加权（Inverse Probability Weighting，简称 IPW）方法来估计研究之间的总体平均值。此外，我们还提出了基于异质性度量（如研究间方差和 I 度量）的 IPW 版本。我们提出了基于渐近正态逼近和参数自举的置信区间构建方法。通过数值实验，我们观察到估计器成功消除了偏差，并且置信区间的经验覆盖率接近名义水平。另一方面，在某些情况下，基于渐近正态逼近的置信区间比自举置信区间宽得多。因此，建议在实际应用中使用后者。
