Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.
Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.
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DOI：
10.1177/0962280214521348
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发表时间：
2015-08
影响因子：
2.3
通讯作者：
Alzheimer's Disease Neuroimaging Initiative*
中科院分区：
文献类型：
--
作者：
Bartlett JW;Seaman SR;White IR;Carpenter JR;Alzheimer's Disease Neuroimaging Initiative*
在流行病学和临床研究中，协变量数据缺失是常见的现象，通常采用多重插补方法进行处理。如果实质性模型是非线性的（例如，考克斯比例风险模型），或包含非线性（例如，平方）或交互作用项，则部分观察到的协变量的插补是复杂的，并且多重插补的标准软件实现可能会插补来自与此类实质性模型不兼容的模型的协变量。我们展示了如何填补完全条件规范，一种流行的方法进行多重填补，可以修改，使协变量是从模型，这是兼容的实质性模型。我们调查，通过模拟的性能，这一建议，并与现有的方法进行比较。模拟结果表明，我们的建议提供了一致的估计范围内常见的实质性模型，包括模型包含非线性协变量的影响或相互作用，提供的数据是随机缺失的，假设插补模型是正确的指定和相互兼容。实现该方法的Stata软件是免费提供的。
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available.