Department of Nephrology, Princess Margaret Hospital, Subiaco, WA, 6008, Australia.
Sydney School of Public Health, University of Sydney, Sydney, Australia.
Pediatr Nephrol. 2019 Feb;34(2):223-231. doi: 10.1007/s00467-018-3932-4. Epub 2018 Mar 13.
Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature.
缺失数据是临床研究中一个重要且常见的偏倚来源。读者在阅读研究时应警惕并考虑缺失数据的影响。除了通过良好的研究设计和实施从一开始就防止缺失数据外，还可以使用不同的策略来处理包含缺失观测值的数据。完全病例分析往往存在偏差，除非数据是完全随机缺失的。处理缺失数据的更好方法包括多重插补和基于似然估计的模型。随着计算能力的提高和现代统计软件的发展，在生物统计学家的指导下，这些方法已经为临床研究人员所掌握。作为阅读论文的临床医生，我们需要不断更新对统计方法的理解，以便了解这些技术的局限性，并能够批判性地解读文献。
