Jolani Shahab, Frank Laurence E, van Buuren Stef
Department of Methodology and Statistics, Utrecht University, The Netherlands.
Br J Math Stat Psychol. 2014 May;67(2):197-212. doi: 10.1111/bmsp.12021. Epub 2013 Aug 5.
Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well-known likelihood-based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing-based methods protect against misspecification bias if one of the models, but not necessarily both, for the data or the mechanism leading to missing data is correct. We propose a new imputation method that captures the simplicity of MI and protection from the DR method. This method integrates MI and DR to protect against misspecification of the imputation model under a missing at random assumption. Our method avoids analytical complications of missing data particularly in multivariate settings, and is easy to implement in standard statistical packages. Moreover, the proposed method works very well with an intermittent pattern of missingness when other DR methods can not be used. Simulation experiments show that the proposed approach achieves improved performance when one of the models is correct. The method is applied to data from the fireworks disaster study, a randomized clinical trial comparing therapies in disaster-exposed children. We conclude that the new method increases the robustness of imputations.
缺失值是纵向数据分析中的一个实际问题。多重填补（MI）是一种著名的基于似然性的方法，如果填补模型指定正确，那么在效率和一致性方面具有最优属性。双稳健（DR）加权法在数据或导致数据缺失的机制的模型之一（不一定是两者）正确的情况下，可防止模型误设偏差。我们提出一种新的填补方法，它兼具多重填补的简单性以及双稳健法的防护性。该方法将多重填补和双稳健法相结合，在随机缺失假设下防止填补模型的误设。我们的方法避免了缺失数据的分析复杂性，尤其是在多变量设置中，并且易于在标准统计软件包中实现。此外，当其他双稳健方法不可用时，该方法对于间歇性缺失模式效果良好。模拟实验表明，当其中一个模型正确时，该方法性能有所提升。该方法应用于烟花灾害研究的数据，这是一项比较灾害暴露儿童不同治疗方法的随机临床试验。我们得出结论，新方法提高了填补的稳健性。
