Missing data result in less precise and possibly biased effect estimates in single studies. Bias arising from studies with incomplete outcome data is naturally propagated in a meta‐analysis. Conventional analysis using only individuals with available data is adequate when the meta‐analyst can be confident that the data are missing at random (MAR) in every study—that is, that the probability of missing data does not depend on unobserved variables, conditional on observed variables. Usually, such confidence is unjustified as participants may drop out due to lack of improvement or adverse effects. The MAR assumption cannot be tested, and a sensitivity analysis to assess how robust results are to reasonable deviations from the MAR assumption is important. Two methods may be used based on plausible alternative assumptions about the missing data. Firstly, the distribution of reasons for missing data may be used to impute the missing values. Secondly, the analyst may specify the magnitude and uncertainty of possible departures from the missing at random assumption, and these may be used to correct bias and reweight the studies. This is achieved by employing a pattern mixture model and describing how the outcome in the missing participants is related to the outcome in the completers. Ideally, this relationship is informed using expert opinion. The methods are illustrated in two examples with binary and continuous outcomes. We provide recommendations on what trial investigators and systematic reviewers should do to minimize the problem of missing outcome data in meta‐analysis.

中文翻译：

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处理荟萃分析中缺失的结果数据。

缺失数据会导致单个研究中的效果估计不太精确且可能存在偏差。结果数据不完整的研究产生的偏差在荟萃分析中自然会传播。当荟萃分析师确信每项研究中数据随机缺失 (MAR) 时，仅使用具有可用数据的个体进行常规分析就足够了，也就是说，缺失数据的概率不依赖于未观察到的变量，条件是观察到的变量。通常，这种信心是不合理的，因为参与者可能会因缺乏改进或不利影响而退出。MAR 假设无法进行检验，因此进行敏感性分析来评估结果对于 MAR 假设的合理偏差的稳健程度非常重要。基于关于缺失数据的合理替代假设，可以使用两种方法。首先，缺失数据的原因分布可用于估算缺失值。其次，分析师可以指定可能偏离随机假设缺失的程度和不确定性，这些可用于纠正偏差并重新衡量研究。这是通过采用模式混合模型并描述缺失参与者的结果与完成者的结果如何相关来实现的。理想情况下，这种关系是通过专家意见来确定的。这些方法通过两个具有二元和连续结果的示例进行说明。我们就试验研究者和系统评价者应采取哪些措施来最大程度地减少荟萃分析中结果数据缺失的问题提供建议。