论文标题
网络荟萃分析中的程度不规则性和等级概率偏差
Degree irregularity and rank probability bias in network meta-analysis
论文作者
论文摘要
网络荟萃分析（NMA）是一种用于比较治疗方案的统计技术。网络的节点是相互竞争的治疗方法，边缘代表了试验中治疗方法的比较。贝叶斯NMA的结果包括治疗效果的估计值，以及每种治疗排名最高，第二好等的概率。网络几何形状如何影响这些结果的准确性和精度尚未完全理解。在这里，我们进行了一项模拟研究，发现涉及不同治疗方法的试验数量的差异会导致估计等级概率的系统偏见。这种偏见与治疗效应估计的精度的差异增加有关。使用复杂网络理论中的思想，我们定义了“程度不规则”的度量，以量化涉及每种治疗的研究数量中的不对称性。我们的模拟表明，更多的规则网络具有更精确的治疗效果估计值，而等级概率的偏差较小。我们还发现，与网络中的研究总数和每个比较的试验数量相比，程度规律性是NMA质量的更好指标。这些结果对计划未来的试验有影响。我们证明，选择减少网络不规则性的试验可以提高NMA结果的精度和准确性。
Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. The nodes of the network are the competing treatments and edges represent comparisons of treatments in trials. Outcomes of Bayesian NMA include estimates of treatment effects, and the probabilities that each treatment is ranked best, second best and so on. How exactly network geometry affects the accuracy and precision of these outcomes is not fully understood. Here we carry out a simulation study and find that disparity in the number of trials involving different treatments leads to a systematic bias in estimated rank probabilities. This bias is associated with an increased variation in the precision of treatment effect estimates. Using ideas from the theory of complex networks, we define a measure of `degree irregularity' to quantify asymmetry in the number of studies involving each treatment. Our simulations indicate that more regular networks have more precise treatment effect estimates and smaller bias of rank probabilities. We also find that degree regularity is a better indicator of NMA quality than both the total number of studies in a network and the disparity in the number of trials per comparison. These results have implications for planning future trials. We demonstrate that choosing trials which reduce the network's irregularity can improve the precision and accuracy of NMA outcomes.
