Madley-Dowd Paul, Curnow Elinor, Hughes Rachael A, Cornish Rosie, Tilling Kate, Heron Jon
Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom.
MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom.
Am J Epidemiol. 2024 Aug 27. doi: 10.1093/aje/kwae306.
Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomplete. We explored how missing data in auxiliary variables influenced estimates obtained from MI. We implemented a simulation study with three different missing data mechanisms for the outcome. We then examined the impact of increasing proportions of missing data and different missingness mechanisms for the auxiliary variable on bias of an unadjusted linear regression coefficient and the fraction of missing information. We illustrate our findings with an applied example in the Avon Longitudinal Study of Parents and Children. We found that where complete records analyses were biased, increasing proportions of missing data in auxiliary variables, under any missing data mechanism, reduced the ability of MI including the auxiliary variable to mitigate this bias. Where there was no bias in the complete records analysis, inclusion of a missing not at random auxiliary variable in MI introduced bias of potentially important magnitude (up to 17% of the effect size in our simulation). Careful consideration of the quantity and nature of missing data in auxiliary variables needs to be made when selecting them for use in MI models.
辅助变量用于多重填补（MI）以减少偏差并提高效率。这些变量本身往往也不完整。我们探讨了辅助变量中的缺失数据如何影响从MI获得的估计值。我们针对结果实施了一项具有三种不同缺失数据机制的模拟研究。然后，我们研究了辅助变量中缺失数据比例的增加以及不同的缺失机制对未调整线性回归系数偏差和缺失信息比例的影响。我们用阿冯亲子纵向研究中的一个应用实例来说明我们的发现。我们发现，在完整记录分析存在偏差的情况下，在任何缺失数据机制下，辅助变量中缺失数据比例的增加都会降低包含该辅助变量的MI减轻这种偏差的能力。在完整记录分析无偏差的情况下，在MI中纳入一个非随机缺失的辅助变量会引入潜在重要程度的偏差（在我们的模拟中高达效应大小的17%）。在选择辅助变量用于MI模型时，需要仔细考虑其缺失数据的数量和性质。
