The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta‐analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between‐trial heterogeneity and including multi‐arm trials. Results of the evaluation of two popular frequentist methods and a Bayesian approach using two different prior specifications are presented. Methods are evaluated using coverage, width of intervals, bias, and root mean squared error (RMSE). In addition, deviations from the theoretical surface under the cumulative rankings (SUCRAs) or P‐scores of the treatments are evaluated. Under low heterogeneity and when a large number of trials informs the contrasts, all methods perform well with respect to the evaluated performance measures. Coverage is observed to be generally higher for the Bayesian than the frequentist methods. The width of credible intervals is larger than those of confidence intervals and is increasing when using a flatter prior for between‐trial heterogeneity. Bias was generally small, but increased with heterogeneity, especially in netmeta. In some scenarios, the direction of bias differed between frequentist and Bayesian methods. The RMSE was comparable between methods but larger in indirectly than in directly estimated treatment effects. The deviation of the SUCRAs or P‐scores from their theoretical values was mostly comparable over the methods but differed depending on the heterogeneity and the geometry of the investigated network. Multivariate meta‐regression or Bayesian estimation using a half‐normal prior scaled to 0.5 seems to be promising with respect to the evaluated performance measures in network meta‐analysis of sparse networks.

中文翻译：

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贝叶斯方法和频频方法在二元数据随机效应网络元分析中的比较。

统计方法的性能通常通过模拟研究来评估。但是，在对二进制数据进行网络元分析的情况下，目前尚无法对许多实际相关的设置进行仿真。我们针对试验间异质性下的稀疏试验网络（包括多臂试验）进行了模拟研究。给出了使用两种不同的先验规范对两种流行的频率论者方法和贝叶斯方法进行评估的结果。使用覆盖率，区间宽度，偏差和均方根误差（RMSE）评估方法。此外，还评估了累积排名（SUCRAs）或治疗P分值与理论表面的偏差。在异质性较低的情况下，当大量试验表明对比时，就评估的绩效指标而言，所有方法均表现良好。通常，贝叶斯方法的覆盖率比频繁方法要高。可信区间的宽度大于置信区间的宽度，并且对于试验间的异质性使用较平坦的先验值时，可信区间的宽度会增大。偏差通常很小，但随着异质性而增加，尤其是在netmeta中。在某些情况下，偏见的方向在贝叶斯方法和贝叶斯方法之间是不同的。方法之间的RMSE相当，但间接比直接估计的治疗效果大。SUCRA或P得分与理论值的偏差在方法上几乎是可比较的，但根据所研究网络的异质性和几何形状而有所不同。