Murad M Hassan, Lin Lifeng
Evidence-based Practice Center, Kern Center for the Science of Healthcare Delivery Mayo Clinic Rochester Minnesota USA.
Department of Epidemiology and Biostatistics University of Arizona Tucson Arizona USA.
Cochrane Evid Synth Methods. 2025 Jan 28;3(2):e70018. doi: 10.1002/cesm.70018. eCollection 2025 Mar.
The common practice in meta-analysis and clinical practice guidelines is to derive the absolute treatment effect (also called risk difference, RD) from a combination of a pooled relative risk (RR) that resulted from a meta-analysis, and a user-provided baseline risk (BR). However, this method does not address the uncertainty in BR. We developed a web-based R Shiny tool to perform simple microsimulation and incorporate uncertainty in BR into the precision of RD. We empirically evaluated this approach by estimating the impact of incorporating this uncertainty when BR is derived from the control group rates in 3,128 meta-analyses curated from the Cochrane Library (26,964 individual studies). When BR was derived from the largest study in each meta-analysis, the median width of the CI of BR was 11.6% (interquartile range (IQR), 6.30%-18.5%). Incorporating this uncertainty in BR led to expansion of the RD CI by a median of 8 per 1,000 persons (IQR 2-24). This expansion increased in a linear fashion with BR imprecision and was more prominent in meta-analyses with low BR. This study provides a web-based tool to perform simple microsimulation and incorporate uncertainty in BR into the CI of RD.
在荟萃分析和临床实践指南中,常见的做法是根据荟萃分析得出的合并相对风险(RR)与用户提供的基线风险(BR)相结合,来推导绝对治疗效果(也称为风险差异,RD)。然而,这种方法并未考虑BR中的不确定性。我们开发了一个基于网络的R Shiny工具,用于进行简单的微观模拟,并将BR中的不确定性纳入RD的精确性评估中。我们通过估计当BR从Cochrane图书馆整理的3128项荟萃分析(26964项个体研究)的对照组发生率中得出时,纳入这种不确定性的影响,对该方法进行了实证评估。当BR从每项荟萃分析中最大的研究得出时,BR的置信区间(CI)的中位数宽度为11.6%(四分位间距(IQR),6.30%-18.5%)。将BR中的这种不确定性纳入后,RD的CI中位数每1000人扩大8(IQR 2-24)。这种扩大与BR的不精确性呈线性增加,并且在BR较低的荟萃分析中更为显著。本研究提供了一个基于网络的工具,用于进行简单的微观模拟,并将BR中的不确定性纳入RD的CI评估中。