【摘要】 缺失数据是临床试验中常见但又不可避免的一个问题。缺失数据不仅会降低试验的把握度,还会给试验结果带来偏倚。因此,一方面可以在统计分析中采用合适的缺失数据处理方法,另一方面要特别注意尽可能预防缺失数据的产生。其中,缺失数据的预防应当是第一位的。从数据的角度来讲,首先,应在方案设计、数据采集和数据核查的各个阶段,采取合理措施提高受试者的依从性,减少不必要的数据缺失;其次,对于确认发生的数据缺失,应详细记录缺失数据产生的原因,这对于判定数据缺失的机制和选择合适的缺失数据处理方法 (例如,前一次观察数据向后结转、多重填补和重复测量数据混合效应模型等)具有非常重要的作用。更多还原

【Abstract】 Missing data is a common but unavoidable issue in clinical trials. It not only lowers the trial power, but brings the bias to the trial results. Therefore, on one hand, the missing data handling methods are employed in data analysis. On the other hand, it is vital to prevent the missing data in the trials. Prevention of missing data should take the first place. From the perspective of data, firstly, some measures should be taken at the stages of protocol design, data collection and data check to enhance the patients’ compliance and reduce the unnecessary missing data. Secondly, the causes of confirmed missing data in the trials should be notified and recorded in detail, which are very important to determine the mechanism of missing data and choose the suitable missing data handling methods, e.g., last observation carried forward(LOCF); multiple imputation(MI); mixed-effect model repeated measure(MMRM), etc.更多还原