Bayesian analyses with the arm‐based (AB) network meta‐analysis (NMA) model require researchers to specify a prior distribution for the covariance matrix of the treatment‐specific event rates in a transformed scale, for example, the treatment‐specific log‐odds when a logit transformation is used. The commonly used conjugate prior for the covariance matrix, the inverse‐Wishart (IW) distribution, has several limitations. For example, although the IW distribution is often described as noninformative or weakly informative, it may in fact provide strong information when some variance components are small (eg, when the standard deviation of study‐specific log‐odds of a treatment is smaller than 1/2), as is common in NMAs with binary outcomes. In addition, the IW prior generally leads to underestimation of correlations between treatment‐specific log‐odds, which are critical for borrowing strength across treatment arms to estimate treatment effects efficiently and to reduce potential bias. Alternatively, several separation strategies (ie, separate priors on variances and correlations) can be considered. To study the IW prior's impact on NMA results and compare it with separation strategies, we did simulation studies under different missing‐treatment mechanisms. A separation strategy with appropriate priors for the correlation matrix and variances performs better than the IW prior, and should be recommended as the default vague prior in the AB NMA approach. Finally, we reanalyzed three case studies and illustrated the importance, when performing AB‐NMA, of sensitivity analyses with different prior specifications on variances.

中文翻译：

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协方差先验对具有二元结果的基于臂的贝叶斯网络元分析的影响。

使用基于臂 (AB) 网络元分析 (NMA) 模型的贝叶斯分析要求研究人员在转换的尺度中指定治疗特定事件率的协方差矩阵的先验分布，例如，治疗特定对数使用 logit 转换时的几率。协方差矩阵常用的共轭先验，即逆-Wishart (IW) 分布，有几个限制。例如，虽然 IW 分布通常被描述为无信息或弱信息，但当一些方差分量很小时（例如，当研究特定对数几率的标准偏差小于 1 时，它实际上可能提供强大的信息/2)，这在具有二元结果的 NMA 中很常见。此外，IW 先验通常会导致低估治疗特定对数几率之间的相关性，这对于跨治疗组借用力量以有效估计治疗效果并减少潜在偏差至关重要。或者，可以考虑几种分离策略（即，方差和相关性的分离先验）。为了研究 IW 先验对 NMA 结果的影响并将其与分离策略进行比较，我们在不同的缺失处理机制下进行了模拟研究。对相关矩阵和方差具有适当先验的分离策略比 IW 先验表现更好，应推荐作为 AB NMA 方法中的默认模糊先验。最后，我们重新分析了三个案例研究，并说明了在执行 AB-NMA 时使用不同的先前方差规范进行敏感性分析的重要性。