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Abstract：
Missing participant outcome data (MOD) are ubiquitous in systematic reviews with network meta-analysis (NMA) as they invade from the inclusion of clinical trials with reported participant losses. There are available strategies to address aggregate MOD, and in particular binary MOD, while considering the missing at random (MAR) assumption as a starting point. Little is known about their performance though regarding the meta-analytic parameters of a random-effects model for aggregate binary outcome data as obtained from trial-reports (i.e. the number of events and number of MOD out of the total randomised per arm).
We used four strategies to handle binary MOD under MAR and we classified these strategies to those modelling versus excluding/imputing MOD and to those accounting for versus ignoring uncertainty about MAR. We investigated the performance of these strategies in terms of core NMA estimates by performing both an empirical and simulation study using random-effects NMA based on electrical network theory. We used Bland-Altman plots to illustrate the agreement between the compared strategies, and we considered the mean bias, coverage probability and width of the confidence interval to be the frequentist measures of performance.
Modelling MOD under MAR agreed with exclusion and imputation under MAR in terms of estimated log odds ratios and inconsistency factor, whereas accountability or not of the uncertainty regarding MOD affected intervention hierarchy and precision around the NMA estimates: strategies that ignore uncertainty about MOD led to more precise NMA estimates, and increased between-trial variance. All strategies showed good performance for low MOD (<5%), consistent evidence and low between-trial variance, whereas performance was compromised for large informative MOD (> 20%), inconsistent evidence and substantial between-trial variance, especially for strategies that ignore uncertainty due to MOD.
The analysts should avoid applying strategies that manipulate MOD before analysis (i.e. exclusion and imputation) as they implicate the inferences negatively. Modelling MOD, on the other hand, via a pattern-mixture model to propagate the uncertainty about MAR assumption constitutes both conceptually and statistically proper strategy to address MOD in a systematic review.
We used four strategies to handle binary MOD under MAR and we classified these strategies to those modelling versus excluding/imputing MOD and to those accounting for versus ignoring uncertainty about MAR. We investigated the performance of these strategies in terms of core NMA estimates by performing both an empirical and simulation study using random-effects NMA based on electrical network theory. We used Bland-Altman plots to illustrate the agreement between the compared strategies, and we considered the mean bias, coverage probability and width of the confidence interval to be the frequentist measures of performance.
Modelling MOD under MAR agreed with exclusion and imputation under MAR in terms of estimated log odds ratios and inconsistency factor, whereas accountability or not of the uncertainty regarding MOD affected intervention hierarchy and precision around the NMA estimates: strategies that ignore uncertainty about MOD led to more precise NMA estimates, and increased between-trial variance. All strategies showed good performance for low MOD (<5%), consistent evidence and low between-trial variance, whereas performance was compromised for large informative MOD (> 20%), inconsistent evidence and substantial between-trial variance, especially for strategies that ignore uncertainty due to MOD.
The analysts should avoid applying strategies that manipulate MOD before analysis (i.e. exclusion and imputation) as they implicate the inferences negatively. Modelling MOD, on the other hand, via a pattern-mixture model to propagate the uncertainty about MAR assumption constitutes both conceptually and statistically proper strategy to address MOD in a systematic review.
摘要：
缺失的参与者结果数据（MOD）在网络荟萃分析（NMA）的系统评价中无处不在，因为它们侵入了包含报告的参与者损失的临床试验。有可用的策略来解决聚合MOD，特别是二进制MOD，同时考虑随机缺失(MAR)假设作为起点。尽管从试验报告中获得的用于汇总二元结果数据的随机效应模型的荟萃分析参数（即每臂随机总数中的事件数和MOD数），但对它们的性能知之甚少。
我们使用了四种策略来处理MAR下的二进制MOD，我们将这些策略分类为建模与排除/估算MOD以及考虑与忽略MAR不确定性的策略。通过使用基于电网络理论的随机效应NMA进行经验和模拟研究，我们根据核心NMA估计研究了这些策略的性能。我们使用Bland-Altman图来说明比较策略之间的一致性，我们考虑了均值偏差，覆盖率和置信区间的宽度是绩效的频率度量。
根据估计的对数优势比和不一致因素，MAR下的MOD建模与MAR下的排除和归因一致，Wherebyaccountabilityornotofthe不确定性regardingMODaffectedinterventionhierarchyandprecisionaroundtheNMAestimates:strategiesthatignoreconcernessaboutMODledtomorepreciseNMAestiments,并增加了试验间的差异。所有策略在低MOD(<5%)下表现良好，一致的证据和低的试验间方差，而大型信息MOD(>20%)的性能受到损害，不一致的证据和巨大的试验间差异，特别是对于忽略MOD带来的不确定性的策略。
分析师应避免在分析之前使用操纵MOD的策略（即排除和归因），因为它们暗示了推论的负面影响。MOD建模,另一方面,通过模式混合模型来传播关于MAR假设的不确定性，在概念上和统计上都构成了在系统评价中解决MOD的适当策略。
我们使用了四种策略来处理MAR下的二进制MOD，我们将这些策略分类为建模与排除/估算MOD以及考虑与忽略MAR不确定性的策略。通过使用基于电网络理论的随机效应NMA进行经验和模拟研究，我们根据核心NMA估计研究了这些策略的性能。我们使用Bland-Altman图来说明比较策略之间的一致性，我们考虑了均值偏差，覆盖率和置信区间的宽度是绩效的频率度量。
根据估计的对数优势比和不一致因素，MAR下的MOD建模与MAR下的排除和归因一致，Wherebyaccountabilityornotofthe不确定性regardingMODaffectedinterventionhierarchyandprecisionaroundtheNMAestimates:strategiesthatignoreconcernessaboutMODledtomorepreciseNMAestiments,并增加了试验间的差异。所有策略在低MOD(<5%)下表现良好，一致的证据和低的试验间方差，而大型信息MOD(>20%)的性能受到损害，不一致的证据和巨大的试验间差异，特别是对于忽略MOD带来的不确定性的策略。
分析师应避免在分析之前使用操纵MOD的策略（即排除和归因），因为它们暗示了推论的负面影响。MOD建模,另一方面,通过模式混合模型来传播关于MAR假设的不确定性，在概念上和统计上都构成了在系统评价中解决MOD的适当策略。
