Pateras Konstantinos, Nikolakopoulos Stavros, Mavridis Dimitris, Roes Kit C B
Department of Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands.
Department of Primary Education, School of Medicine, University of Ioannina, University Campus, 45110 Ioannina, Greece.
Contemp Clin Trials Commun. 2018 Jan 9;9:98-107. doi: 10.1016/j.conctc.2017.11.012. eCollection 2018 Mar.
When a meta-analysis consists of a few small trials that report zero events, accounting for heterogeneity in the (interval) estimation of the overall effect is challenging. Typically, we predefine meta-analytical methods to be employed. In practice, data poses restrictions that lead to deviations from the pre-planned analysis, such as the presence of zero events in at least one study arm. We aim to explore heterogeneity estimators behaviour in estimating the overall effect across different levels of sparsity of events. We performed a simulation study that consists of two evaluations. We considered an overall comparison of estimators unconditional on the number of observed zero cells and an additional one by conditioning on the number of observed zero cells. Estimators that performed modestly robust when (interval) estimating the overall treatment effect across a range of heterogeneity assumptions were the Sidik-Jonkman, Hartung-Makambi and improved Paul-Mandel. The relative performance of estimators did not materially differ between making a predefined or data-driven choice. Our investigations confirmed that heterogeneity in such settings cannot be estimated reliably. Estimators whose performance depends strongly on the presence of heterogeneity should be avoided. The choice of estimator does not need to depend on whether or not zero cells are observed.
当一项荟萃分析由一些报告零事件的小型试验组成时，在（区间）估计总体效应时考虑异质性具有挑战性。通常，我们会预先定义要采用的荟萃分析方法。在实际操作中，数据会带来一些限制，导致与预先计划的分析产生偏差，例如至少一个研究组中出现零事件。我们旨在探讨异质性估计量在估计不同事件稀疏程度下的总体效应时的表现。我们进行了一项包含两项评估的模拟研究。我们考虑了对估计量进行总体比较，一种是不考虑观察到的零单元格数量，另一种是通过考虑观察到的零单元格数量进行额外比较。在一系列异质性假设下（区间）估计总体治疗效应时表现出适度稳健性的估计量有西迪克 - 琼克曼估计量、哈通 - 马坎比估计量和改进的保罗 - 曼德尔估计量。在进行预定义选择或数据驱动选择时，估计量的相对表现没有实质性差异。我们的研究证实，在这种情况下异质性无法可靠估计。应避免使用其性能强烈依赖异质性存在的估计量。估计量的选择无需取决于是否观察到零单元格。
