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Abstract：
Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network meta-analysis model for one aggregate continuous outcome to incorporate a missingness parameter that measures the departure from the missing at random assumption. We consider various effect size estimates for continuous data, and two informative missingness parameters, the informative missingness difference of means and the informative missingness ratio of means. We incorporate our prior belief about the missingness parameters while allowing for several possibilities of prior structures to account for the fact that the missingness process may differ in the network. The method is exemplified in two networks from published reviews comprising a different amount of missing continuous outcome data.
摘要：
有必要正确处理总体缺失结果数据，以最大程度地减少系统评价结论中的偏见。已经提出了两阶段模式混合模型来解决聚合丢失的连续结果数据。虽然这种方法与排除缺失的连续结果数据和简单的插补方法相比更合适，它没有提供对缺失的连续结果数据的灵活建模，以彻底调查它们对结论的影响。因此,我们提出了贝叶斯框架下的一阶段模式混合模型方法,以解决干预网络中缺失的连续结果数据,并获得关于不同试验和干预中的错误过程的知识.我们扩展了一个汇总连续结果的分层网络元分析模型，以包含一个错误参数，该参数可以衡量与随机假设的偏离。我们考虑连续数据的各种效应大小估计，和两个信息错误参数，手段的信息性错误差异和手段的信息性错误比率。我们结合了先前对错误参数的信念，同时考虑了先前结构的几种可能性，以说明网络中错误过程可能不同的事实。该方法在来自已发表的评论的两个网络中进行了示例，这些评论包括不同数量的缺失连续结果数据。
