The impact of covariance priors on arm-based Bayesian network meta-analyses with binary outcomes
The impact of covariance priors on arm-based Bayesian network meta-analyses with binary outcomes
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DOI：
10.1002/sim.8580
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发表时间：
2020-06-03
影响因子：
2
通讯作者：
Chu, Haitao
中科院分区：
文献类型：
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作者：
Wang, Zhenxun;Lin, Lifeng;Chu, Haitao
使用基于臂（AB）的网络荟萃分析（NMA）模型的贝叶斯分析要求研究者在转换量表中指定治疗特异性事件发生率协方差矩阵的先验分布，例如，使用logit转换时的治疗特异性对数比值。常用的协方差矩阵的共轭先验，逆Wishart（IW）分布，有几个限制。例如，尽管IW分布通常被描述为无信息或弱信息，但当某些方差分量较小时（例如，当治疗的研究特定对数比值的标准差小于1/2时），它实际上可能提供强信息，这在具有二元结局的NMA中很常见。此外，IW先验通常会导致低估治疗特异性对数比值之间的相关性，这对于在治疗组间借用强度以有效估计治疗效果并减少潜在偏倚至关重要。或者，可以考虑几种分离策略（即，方差和相关性的单独先验）。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，我们在不同的遗漏处理机制下进行了模拟研究。一个分离策略与适当的相关矩阵和方差的先验表现优于IW先验，并应推荐作为默认的模糊先验的AB NMA方法。最后，我们重新分析了三个案例研究和说明的重要性，当执行AB-NMA，灵敏度分析与不同的先验规格的方差。
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.