Rippe Ralph C A, den Heijer Martin, le Cessie Saskia
LUMC, afd. Klinische Epidemiologie, Leiden, the Netherlands.
Ned Tijdschr Geneeskd. 2013;157(18):A5539.
In medical research missing data are sometimes inevitable. Different missingness mechanisms can be distinguished: (a) missing completely at random; (b) missing by design; (c) missing at random, and (d) missing not at random. If participants with missing data are excluded from statistical analyses, this can lead to biased study results and loss of statistical power. Imputation methods can be applied to estimate missing values; multiple imputation gives a good idea of the inaccuracy of the reconstructed measurements. The most common imputation methods assume that missing data are missing at random. Multiple imputation contributes greatly to the efficiency and reliability of estimates because maximum use is made of the data collected. Imputation is not meant to obviate low-quality data.
在医学研究中，缺失数据有时是不可避免的。可以区分不同的缺失机制：（a）完全随机缺失；（b）设计性缺失；（c）随机缺失；以及（d）非随机缺失。如果将有缺失数据的参与者排除在统计分析之外，这可能会导致有偏差的研究结果并损失统计效力。插补方法可用于估计缺失值；多重插补能很好地了解重构测量值的不准确性。最常见的插补方法假定缺失数据是随机缺失的。多重插补极大地提高了估计的效率和可靠性，因为充分利用了所收集的数据。插补并非旨在消除低质量数据。
