School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Res Synth Methods. 2021 Mar;12(2):248-259. doi: 10.1002/jrsm.1468. Epub 2020 Nov 18.
A P value, or the magnitude or direction of results can influence decisions about whether, when, and how research findings are disseminated. Regardless of whether an entire study or a particular study result is unavailable because investigators considered the results to be unfavorable, bias in a meta-analysis may occur when available results differ systematically from missing results. In this article, we summarize the empirical evidence for various reporting biases that lead to study results being unavailable for inclusion in systematic reviews, with a focus on health research. These biases include publication bias and selective nonreporting bias. We describe processes that systematic reviewers can use to minimize the risk of bias due to missing results in meta-analyses of health research, such as comprehensive searches and prospective approaches to meta-analysis. We also outline methods that have been designed for assessing risk of bias due to missing results in meta-analyses of health research, including using tools to assess selective nonreporting of results, ascertaining qualitative signals that suggest not all studies were identified, and generating funnel plots to identify small-study effects, one cause of which is reporting bias. HIGHLIGHTS: Bias in a meta-analysis may occur when available results differ systematically from missing results. Several different tools, plots, and statistical methods have been designed for assessing risk of bias due to missing results in meta-analyses. These include comparison of prespecified analysis plans with completed reports to detect selective nonreporting of results, consideration of qualitative signals that suggest not all studies were identified, and the use of funnel plots to identify small-study effects, for which reporting bias is one of several causes. Information from approaches such as funnel plots and selection models is more difficult to interpret than from less subjective approaches such as detection of incompletely reported results in studies for which prespecified analysis plans were available.
P 值或结果的大小或方向可能会影响有关是否、何时以及如何传播研究结果的决策。无论整个研究或特定研究结果是否由于研究人员认为结果不利而无法获得，当可用结果与缺失结果系统地不同时，荟萃分析中可能会出现偏倚。在本文中，我们总结了导致研究结果无法纳入系统评价的各种报告偏倚的经验证据，重点是健康研究。这些偏倚包括发表偏倚和选择性报告偏倚。我们描述了系统评价者可以用来最小化由于健康研究荟萃分析中缺失结果导致的偏倚风险的过程，例如全面搜索和前瞻性荟萃分析方法。我们还概述了旨在评估健康研究荟萃分析中由于缺失结果导致的偏倚风险的方法，包括使用工具评估结果的选择性报告，确定表明并非所有研究都被识别的定性信号，以及生成漏斗图以识别小研究效应，其中一个原因是报告偏倚。要点：当可用结果与缺失结果系统地不同时，荟萃分析中可能会出现偏倚。已经设计了几种不同的工具、图表和统计方法来评估荟萃分析中由于缺失结果导致的偏倚风险。这些方法包括将预定的分析计划与完成的报告进行比较以检测结果的选择性报告，考虑表明并非所有研究都被识别的定性信号，以及使用漏斗图识别小研究效应，其中报告偏倚是几个原因之一。与检测具有可用预定分析计划的研究中未完全报告的结果等较少主观方法相比，来自漏斗图和选择模型等方法的信息更难解释。
