Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.
Department of Basic Psychology & Methodology, Faculty of Psychology, University of Murcia, Murcia, Spain.
Res Synth Methods. 2020 Jan;11(1):91-104. doi: 10.1002/jrsm.1371. Epub 2019 Aug 22.
This paper considers the problem in aggregate data meta-analysis of studies reporting multiple competing binary outcomes and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared parameter model on hazard ratio scale accounting for different data summaries and competing risks. We adapt theoretical arguments from the literature to demonstrate that the models are equivalent if events are rare. We use constructed data examples and a simulation study to find an event rate threshold of approximately 0.2 above which competing risks and different data summaries may bias results if no adjustments are made. Below this threshold, simpler models may be sufficient. We recommend analysts to consider the absolute event rates and only use a simple model ignoring data types and competing risks if all of underlying events are rare (below our threshold of approximately 0.2). If one or more of the absolute event rates approaches or exceeds our informal threshold, it may be necessary to account for data types and competing risks through a shared parameter model in order to avoid biased estimates.
本文考虑了在报告多种竞争性二分类结局的研究的汇总数据分析中以及在使用不同汇总格式报告这些结局的研究中存在的问题。例如，一些研究可能报告至少有一种每种结局的患者人数，而另一些研究可能报告所有这些结局的总数。我们在风险比尺度上开发了一个共享参数模型，以考虑不同的数据汇总和竞争风险。我们从文献中的理论观点出发进行论证，如果事件很少，这些模型是等效的。我们使用构造的数据示例和模拟研究来发现一个事件率阈值，约为 0.2 以上，如果不进行调整，竞争风险和不同的数据汇总可能会产生偏差。在这个阈值以下，更简单的模型可能就足够了。我们建议分析人员考虑绝对事件率，如果所有潜在事件都很少（低于我们约 0.2 的阈值），则只考虑使用忽略数据类型和竞争风险的简单模型。如果一个或多个绝对事件率接近或超过我们的非正式阈值，则可能需要通过共享参数模型来考虑数据类型和竞争风险，以避免有偏差的估计。
