General Medicine Section, Zablocki VAMC, Milwaukee, WI, USA.
Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA.
J Gen Intern Med. 2022 Apr;37(5):1247-1253. doi: 10.1007/s11606-021-07135-3. Epub 2021 Oct 19.
Selective or non-reporting of study outcomes results in outcome reporting bias.
We sought to develop and assess tools for detecting and adjusting for outcome reporting bias.
Using data from a previously published systematic review, we abstracted whether outcomes were reported as collected, whether outcomes were statistically significant, and whether statistically significant outcomes were more likely to be reported. We proposed and tested a model to adjust for unreported outcomes and compared our model to three other methods (Copas, Frosi, trim and fill). Our approach assumes that unreported outcomes had a null intervention effect with variance imputed based on the published outcomes. We further compared our approach to these models using simulation, and by varying levels of missing data and study sizes.
There were 286 outcomes reported as collected from 47 included trials: 142 (48%) had the data provided and 144 (52%) did not. Reported outcomes were more likely to be statistically significant than those collected but for which data were unreported and for which non-significance was reported (RR, 2.4; 95% CI, 1.9 to 3.0). Our model and the Copas model provided similar decreases in the pooled effect sizes in both the meta-analytic data and simulation studies. The Frosi and trim and fill methods performed poorly.
Single intervention of a single disease with only randomized controlled trials; approach may overestimate outcome reporting bias impact.
There was evidence of selective outcome reporting. Statistically significant outcomes were more likely to be published than non-significant ones. Our simple approach provided a quick estimate of the impact of unreported outcomes on the estimated effect. This approach could be used as a quick assessment of the potential impact of unreported outcomes.
选择性或不报告研究结果会导致结果报告偏倚。
我们旨在开发和评估用于检测和调整结果报告偏倚的工具。
利用先前发表的系统评价中的数据，我们摘录了结果是否按已收集的方式报告、结果是否具有统计学意义以及具有统计学意义的结果是否更有可能被报告。我们提出并测试了一种调整未报告结果的模型，并将我们的模型与其他三种方法（Copas、Frosi、trim 和 fill）进行了比较。我们的方法假设未报告的结果具有零干预效应，并且方差根据已发表的结果进行推断。我们通过模拟以及不同程度的缺失数据和研究规模，进一步将我们的方法与这些模型进行了比较。
47 项纳入试验中有 286 个结果被报告为已收集：142 个（48%）提供了数据，144 个（52%）未提供。报告的结果比未报告数据且报告为非显著性的结果更有可能具有统计学意义（RR，2.4；95%CI，1.9 至 3.0）。我们的模型和 Copas 模型在荟萃分析数据和模拟研究中都提供了相似的估计效应大小的降低。Frosi 和 trim 和 fill 方法表现不佳。
单一干预措施，单一疾病，仅随机对照试验；方法可能高估结果报告偏倚的影响。
存在选择性结果报告的证据。具有统计学意义的结果比非显著性结果更有可能被发表。我们的简单方法提供了对未报告结果对估计效应影响的快速估计。这种方法可以作为对未报告结果潜在影响的快速评估。
