Abstract：
Missing outcome data in clinical trials may jeopardize the validity of the trial results and inferences for clinical practice. Although sick and preterm newborns are treated as a captive patient population during their stay in the NICUs, their long-term outcomes are often ascertained after discharge. This greatly increases the risk of attrition. We surveyed recently published perinatal and neonatal randomized trials in 7 high-impact general medical and pediatric journals to review the handling of missing primary outcome data and any choice of imputation methods. Of 87 eligible trials in this survey, 77 (89%) had incomplete primary outcome data. The missing outcome data were not discussed at all in 9 reports (12%). Most study teams restricted their main analysis to participants with complete information for the primary outcome (61 trials; 79%). Only 38 of the 77 teams (49%) performed sensitivity analyses using a variety of imputation methods. We conclude that the handling of missing primary outcome data was frequently inadequate in recent randomized perinatal and neonatal trials. To improve future approaches to missing outcome data, we discuss the strengths and limitations of different imputation methods, the appropriate estimation of sample size, and how to deal with data withdrawal. However, the best strategy to reduce bias from missing outcome data in perinatal and neonatal trials remains prevention. Investigators should anticipate and preempt missing data through careful study design, and closely monitor all incoming primary outcome data for completeness during the conduct of the trial.
摘要：
临床试验中缺少结果数据可能会危及试验结果的有效性和临床实践的推论。尽管患病和早产新生儿在NICU逗留期间被视为圈养患者群体，他们的长期结局通常在出院后确定.这大大增加了损耗的风险。我们调查了最近在7个高影响力的普通医学和儿科期刊上发表的围产期和新生儿随机试验，以审查对缺失的主要结局数据的处理以及任何选择的归因方法。在这项调查中的87项符合条件的试验中，77（89％）的主要结果数据不完整。9份报告(12%)根本没有讨论缺失的结果数据。大多数研究团队将他们的主要分析限制在具有主要结局完整信息的参与者(61项试验;79%)。77个团队中只有38个（49％）使用各种估算方法进行了敏感性分析。我们得出的结论是，在最近的随机围产期和新生儿试验中，对缺失的主要结局数据的处理经常不足。为了改进未来对缺失结果数据的方法，我们讨论了不同插补方法的优点和局限性，对样本量的适当估计，以及如何处理数据撤回。然而,在围产期和新生儿试验中,减少结局数据缺失偏倚的最佳策略仍然是预防.调查人员应通过仔细的研究设计来预测和预防丢失的数据，并在试验进行期间密切监测所有传入的主要结局数据的完整性.
