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Abstract：
To investigate the implications of addressing informative missing binary outcome data (MOD) on network meta-analysis (NMA) estimates while applying the missing at random (MAR) assumption under different prior structures of the missingness parameter.
In three motivating examples, we compared six different prior structures of the informative missingness odds ratio (IMOR) parameter in logarithmic scale under pattern-mixture and selection models. Then, we simulated 1000 triangle networks of two-arm trials assuming informative MOD related to interventions. We extended the Bayesian random-effects NMA model for binary outcomes and node-splitting approach to incorporate these 12 models in total. With interval plots, we illustrated the posterior distribution of log OR, common between-trial variance (τ2 ), inconsistency factor and probability of being best per intervention under each model.
All models gave similar point estimates for all NMA estimates regardless of simulation scenario. For moderate and large MOD, intervention-specific prior structure of log IMOR led to larger posterior standard deviation of log ORs compared to trial-specific and common-within-network prior structures. Hierarchical prior structure led to slightly more precise τ2 compared to identical prior structure, particularly for moderate inconsistency and large MOD. Pattern-mixture and selection models agreed for all NMA estimates.
Analyzing informative MOD assuming MAR with different prior structures of log IMOR affected mainly the precision of NMA estimates. Reviewers should decide in advance on the prior structure of log IMOR that best aligns with the condition and interventions investigated.
In three motivating examples, we compared six different prior structures of the informative missingness odds ratio (IMOR) parameter in logarithmic scale under pattern-mixture and selection models. Then, we simulated 1000 triangle networks of two-arm trials assuming informative MOD related to interventions. We extended the Bayesian random-effects NMA model for binary outcomes and node-splitting approach to incorporate these 12 models in total. With interval plots, we illustrated the posterior distribution of log OR, common between-trial variance (τ2 ), inconsistency factor and probability of being best per intervention under each model.
All models gave similar point estimates for all NMA estimates regardless of simulation scenario. For moderate and large MOD, intervention-specific prior structure of log IMOR led to larger posterior standard deviation of log ORs compared to trial-specific and common-within-network prior structures. Hierarchical prior structure led to slightly more precise τ2 compared to identical prior structure, particularly for moderate inconsistency and large MOD. Pattern-mixture and selection models agreed for all NMA estimates.
Analyzing informative MOD assuming MAR with different prior structures of log IMOR affected mainly the precision of NMA estimates. Reviewers should decide in advance on the prior structure of log IMOR that best aligns with the condition and interventions investigated.
摘要：
研究在不同先验结构下应用随机缺失（MAR）假设时，解决信息性缺失二进制结果数据（MOD）对网络荟萃分析（NMA）估计的影响。
在三个激励的例子中，我们在模式混合模型和选择模型下，在对数标度中比较了信息性错误比值比（IMOR）参数的六个不同先验结构。然后，我们模拟了1000个两臂试验的三角网络,假设MOD与干预措施相关.我们将贝叶斯随机效应NMA模型扩展为二元结果和节点拆分方法，以合并总共12个模型。使用间隔图，我们说明了对数或的后验分布，试验间共同方差(τ2)，在每个模型下，不一致因素和每次干预最佳的概率。
所有模型对所有NMA估计都给出了相似的点估计，无论模拟场景如何。对于中等和大型MOD，与试验特异性和网络内共有的先验结构相比,干预特异性的对数IMOR先验结构导致对数OR的后标准偏差更大.与相同的先前结构相比，分层先前结构导致略微更精确的τ2，特别是对于中度不一致和大MOD。所有NMA估计都同意模式混合和选择模型。
分析信息MOD假设具有不同的对数IMOR先验结构的MAR主要影响NMA估计的精度。审稿人应事先决定与所调查的状况和干预措施最吻合的日志IMOR的先前结构。
在三个激励的例子中，我们在模式混合模型和选择模型下，在对数标度中比较了信息性错误比值比（IMOR）参数的六个不同先验结构。然后，我们模拟了1000个两臂试验的三角网络,假设MOD与干预措施相关.我们将贝叶斯随机效应NMA模型扩展为二元结果和节点拆分方法，以合并总共12个模型。使用间隔图，我们说明了对数或的后验分布，试验间共同方差(τ2)，在每个模型下，不一致因素和每次干预最佳的概率。
所有模型对所有NMA估计都给出了相似的点估计，无论模拟场景如何。对于中等和大型MOD，与试验特异性和网络内共有的先验结构相比,干预特异性的对数IMOR先验结构导致对数OR的后标准偏差更大.与相同的先前结构相比，分层先前结构导致略微更精确的τ2，特别是对于中度不一致和大MOD。所有NMA估计都同意模式混合和选择模型。
分析信息MOD假设具有不同的对数IMOR先验结构的MAR主要影响NMA估计的精度。审稿人应事先决定与所调查的状况和干预措施最吻合的日志IMOR的先前结构。
