Resche-Rigon Matthieu, White Ian R, Bartlett Jonathan W, Peters Sanne A E, Thompson Simon G
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.; DBIM, Hôpital Saint-Louis, APHP, Paris, France; Université Paris Diderot, Paris, France; Inserm UMRS 717, Paris, France.
Stat Med. 2013 Dec 10;32(28):4890-905. doi: 10.1002/sim.5894. Epub 2013 Jul 16.
A variable is 'systematically missing' if it is missing for all individuals within particular studies in an individual participant data meta-analysis. When a systematically missing variable is a potential confounder in observational epidemiology, standard methods either fail to adjust the exposure-disease association for the potential confounder or exclude studies where it is missing. We propose a new approach to adjust for systematically missing confounders based on multiple imputation by chained equations. Systematically missing data are imputed via multilevel regression models that allow for heterogeneity between studies. A simulation study compares various choices of imputation model. An illustration is given using data from eight studies estimating the association between carotid intima media thickness and subsequent risk of cardiovascular events. Results are compared with standard methods and also with an extension of a published method that exploits the relationship between fully adjusted and partially adjusted estimated effects through a multivariate random effects meta-analysis model. We conclude that multiple imputation provides a practicable approach that can handle arbitrary patterns of systematic missingness. Bias is reduced by including sufficient between-study random effects in the imputation model.
在个体参与者数据荟萃分析中，如果某个变量在特定研究中的所有个体中均缺失，则该变量为“系统性缺失”。当系统性缺失变量在观察性流行病学中是潜在混杂因素时，标准方法要么无法针对潜在混杂因素调整暴露-疾病关联，要么排除该变量缺失的研究。我们提出了一种基于链式方程多重填补法来调整系统性缺失混杂因素的新方法。系统性缺失数据通过允许研究间存在异质性的多水平回归模型进行填补。一项模拟研究比较了填补模型的各种选择。使用八项研究的数据进行了实例说明，这些研究估计了颈动脉内膜中层厚度与随后心血管事件风险之间的关联。将结果与标准方法以及一种已发表方法的扩展进行了比较，该扩展方法通过多变量随机效应荟萃分析模型利用完全调整和部分调整估计效应之间的关系。我们得出结论，多重填补提供了一种可行的方法，能够处理系统性缺失的任意模式。通过在填补模型中纳入足够的研究间随机效应可减少偏差。
