Key Laboratory for Population Health Across-Life Cycle (Anhui Medical University), Ministry of Education, Anhui, China.
School of Public Health, Anhui Medical University, Anhui, China.
BMC Med Res Methodol. 2023 Mar 13;23(1):61. doi: 10.1186/s12874-023-01884-x.
In evidence synthesis practice, dealing with studies with no cases in both arms has been a tough problem, for which there is no consensus in the research community. In this study, we propose a method to measure the potential impact of studies with no cases for meta-analysis results which we define as harms index (Hi) and benefits index (Bi) as an alternative solution for deciding how to deal with such studies.
Hi and Bi are defined by the minimal number of cases added to the treatment arm (Hi) or control arm (Bi) of studies with no cases in a meta-analysis that lead to a change of the direction of the estimates or its statistical significance. Both exact and approximating methods are available to calculate Hi and Bi. We developed the "hibi" module in Stata so that researchers can easily implement the method. A real-world investigation of meta-analyses from Cochrane reviews was employed to evaluate the proposed method.
Based on Hi and Bi, our results suggested that 21.53% (Hi) to 26.55% (Bi) of Cochrane meta-analyses may be potentially impacted by studies with no cases, for which studies with no cases could not be excluded from the synthesis. The approximating method shows excellent specificity (100%) for both Hi and Bi, moderate sensitivity (68.25%) for Bi, and high sensitivity (80.61%) for Hi compared to the exact method.
The proposed method is practical and useful for systematic reviewers to measure whether studies with no cases impact the results of meta-analyses and may act as an alternative solution for review authors to decide whether to include studies with no events for the synthesis or not.
在证据综合实践中，处理双臂均无病例的研究一直是一个难题，研究界对此尚无共识。本研究提出了一种衡量双臂均无病例的研究对荟萃分析结果的潜在影响的方法，我们将其定义为危害指数（Hi）和获益指数（Bi），作为决定如何处理此类研究的另一种解决方案。
Hi 和 Bi 是通过在荟萃分析中无病例的研究的治疗臂（Hi）或对照臂（Bi）中添加最小数量的病例来定义的，这些病例会导致估计值的方向或其统计意义发生变化。可使用精确和近似方法来计算 Hi 和 Bi。我们在 Stata 中开发了“hibi”模块，以便研究人员可以轻松实现该方法。我们采用了 Cochrane 综述的荟萃分析的实际调查来评估所提出的方法。
基于 Hi 和 Bi，我们的结果表明，21.53%（Hi）至 26.55%（Bi）的 Cochrane 荟萃分析可能受到无病例研究的潜在影响，因此无法将无病例研究排除在综合分析之外。近似方法对 Hi 和 Bi 的特异性均为 100%，对 Bi 的敏感性为 68.25%，对 Hi 的敏感性为 80.61%，与精确方法相比。
所提出的方法对于系统评价者来说是实用且有用的，可衡量无病例研究是否影响荟萃分析的结果，并可能作为评论作者的另一种解决方案，决定是否将无事件的研究纳入综合分析。
