BACKGROUND 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). METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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在频繁性网络荟萃分析中比较缺少的二元结果数据的排除，归因和建模。

背景技术缺少参与者结局数据（MOD）在通过网络荟萃分析（NMA）进行的系统评价中无处不在，因为它们从包含报告的参与者损失的临床试验中侵入。在考虑随机丢失（MAR）假设作为起点的同时，有一些策略可用于处理总MOD，尤其是二进制MOD。尽管对于从试验报告中获得的汇总二元结果数据的随机效应模型的荟萃分析参数（即每组随机事件中事件的数量和MOD的数量），对它们的性能知之甚少。方法我们使用了四种策略来处理MAR下的二进制MOD，并将这些策略分为建模与排除/插补MOD以及考虑与忽略MAR不确定性的策略。我们通过使用基于电气网络理论的随机效应NMA进行了实证研究和仿真研究，对核心NMA估计方面的这些策略的性能进行了调查。我们使用Bland-Altman图说明了所比较策略之间的一致性，并且我们认为平均偏差，覆盖概率和置信区间的宽度是绩效的频繁度量。结果MAR的MOD建模在估计对数比值比和不一致因素方面与MAR的排除和插补相一致，而对MOD不确定性的问责制或非问责制影响了干预水平和NMA估计的准确性：忽略MOD不确定性的策略导致了更精确的NMA估算值，并增加了审判之间的差异。所有策略在MOD较低（<5％），证据一致且试验间差异较低的情况下均表现出良好的性能，而在MOD信息量较大（> 20％），证据不一致和试验间差异较大的情况下，性能会受到影响，尤其是对于那些忽略由于MOD引起的不确定性。结论分析人员应避免在分析之前应用操纵MOD的策略（即排除和归因），因为它们会对推论产生负面影响。另一方面，通过模式混合模型对MOD进行建模以传播有关MAR假设的不确定性，既构成概念上又在统计上适当的策略，以系统地应对MOD。不一致的证据和重大的审判间差异，尤其是对于由于MOD而忽略不确定性的策略。结论分析人员应避免在分析之前应用操纵MOD的策略（即排除和归因），因为它们会对推论产生负面影响。另一方面，通过模式混合模型对MOD进行建模以传播有关MAR假设的不确定性，既构成概念上又在统计上适当的策略，以系统地论述MOD。不一致的证据和重大的审判间差异，尤其是对于由于MOD而忽略不确定性的策略。结论分析人员应避免在分析之前应用操纵MOD的策略（即排除和归因），因为它们会对推论产生负面影响。另一方面，通过模式混合模型对MOD进行建模以传播有关MAR假设的不确定性，既构成概念上又在统计上适当的策略，以系统地论述MOD。