Missing values in longitudinal dietary data: A multiple imputation approach based on a fully conditional specification
Missing values in longitudinal dietary data: A multiple imputation approach based on a fully conditional specification
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DOI：
10.1002/sim.3731
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发表时间：
2009-12-01
影响因子：
2
通讯作者：
Virtanen, Suvi A.
中科院分区：
文献类型：
--
作者：
Nevalainen, Jaakko;Kenward, Michael G.;Virtanen, Suvi A.
多重插补（MI）作为解决观察和对照研究中缺失数据问题的灵活工具越来越受到关注。我们的目标是为糖尿病预测和预防营养研究开发一种有效且高效的 MI 程序，其中通过幼儿期 3 天的食物记录重复测量一组 HLA-DQB1 赋予 I 型糖尿病易感性的新生儿的饮食。风险估计基于队列内的嵌套病例对照设计设置。我们使用了称为完全条件规范（FCS）的迭代程序来为缺失的饮食数据生成适当的值，在这里扮演时间依赖性协变量的角色。我们的方法将标准 FCS 扩展到重复测量设置，通过在个体的后续时间上进行双重迭代，可能出现非单调缺失模式。此外，我们提出的过程是非参数的，因为变量的分布可能严重偏离正态性：它利用分位数正态分数转换为正态性，执行插补，然后转换回原始尺度。通过使用移动时间窗口和逐步回归程序，两倍 FCS 方法可以很好地处理大量变量，每个变量都随时间重复测量。广泛的模拟研究表明，该过程与所提出的转换和变量选择方法一起为嵌套病例对照设置中的有效且高效的统计推断提供了工具，并且其应用范围超出了这一范围。版权所有 (C) 2009 John Wiley & Sons, Ltd.
Multiple imputation (MI) has increasingly received attention as a flexible tool to resolve missing data problems both in observational and controlled studies. Our goal has been to develop a valid and efficient MI procedure for the Diabetes Prediction and Prevention Nutrition Study, in which the diet of a cohort of newborn children with HLA-DQB1-conferred susceptibility to type I diabetes is repeatedly measured by 3-day food records over early childhood. The estimation of risk is based on a nested case-control design setup within the cohort. We have used an iterative procedure known as the fully conditional specification (FCS) to generate appropriate values for the missing dietary data, here playing the role of time-dependent covariates. Our method extends the standard FCS to repeated measurements settings with the possibility of non-monotone missingness patterns by being doubly iterative over the follow-up time of the individuals. In addition, our proposed procedure is nonparametric in the sense that the variables can have distributions deviating strongly from normality: it makes use of quantile normal scores to transform to normality, performs imputations, and transforms back to the original scale. By the use of a moving time window and stepwise regression procedures, the two-fold FCS method operates well with a great number of variables each measured repeatedly over time. Extensive simulation studies demonstrate that the procedure together with the proposed transformations and variable selection methods provides tools for valid and efficient statistical inference in the nested case-control setting, and its applications extend beyond that. Copyright (C) 2009 John Wiley & Sons, Ltd.