Adjusting for Outcome Reporting Bias in Meta-analysis: A Multiple Imputation Approach
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用多重插补调整元分析中的结局报告偏倚
- 研究问题
- 当研究根据结果选择性地不报告结局时，如何调整单变量和多变量元分析，并评估总体治疗效应对这种缺失机制的敏感性？
- 方法与思路
- 作者把未报告结局视为缺失数据，在假定的选择性不报告机制下进行多重插补；多变量方法联合建模相关结局，并以临床数据和模拟评估。
- 材料陈述的贡献
- 摘要称，临床应用的调整结果比朴素分析更不极端，调整幅度随选择机制和异质性变化；多变量版本允许在相关结局之间借用信息。
- 阅读关注点
- 应核对选择性不报告机制如何设定与校准、插补模型如何处理结局相关性，以及不确定性如何合并；重点检查敏感性分析的覆盖范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；调整依赖假定的缺失机制，摘要未说明其可识别性，不能把调整结果视为已消除偏倚。
原文摘要
Background: Outcome reporting bias (ORB) occurs when study outcomes are selectively reported based on their results. ORB potentially undermines the credibility and validity of meta-analyses and contributes to research waste by distorting overall treatment effects. ORB can be viewed as a missing data problem in which unreported study outcomes introduce bias. Despite the serious implications ORB poses, it remains an underrecognized issue, with only a few adjustment methods available. Methods: We propose an approach that addresses unreported study outcomes in meta-analyses through multiple imputation for univariate and multivariate meta-analysis. To assess the impact of ORB in meta-analyses, we apply our proposed methodology to real clinical data affected by ORB, and conduct a simulation study to evaluate the method's performance under a range of scenarios. Results: The proposed method provides bias-adjusted estimates under assumed selective non-reporting mechanisms. In the application to clinical data, ORB-adjusted estimates were systematically shifted towards less extreme treatment effects compared with naive analyses, highlighting the potential magnitude of ORB in practice. The simulation study shows that the extent of adjustment depends on the assumed selection mechanism and the degree of heterogeneity, with stronger selection leading to larger adjustment. Conclusions: Imputing unreported study outcomes provides a promising approach to address ORB in meta-analyses. The multivariate approach extends ORB adjustment to jointly model correlated outcomes, allowing borrowing of strength across outcomes. Overall, we propose a practical and flexible approach for evaluating the sensitivity of univariate and multivariate meta-analytic conclusions to ORB.