Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models.
Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models.
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在分类回归模型中，二进制/分类协变量的二进制/分类协变量的多重估算。
DOI：
10.1002/sim.8004
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发表时间：
2019-02-28
影响因子：
2
通讯作者：
Petersen I
中科院分区：
文献类型：
--
作者：
Pham TM;Carpenter JR;Morris TP;Wood AM;Petersen I
多重插补（MI）已成为流行的医学研究中的缺失数据的分析。MI的标准实施基于数据随机缺失（MAR）的假设。然而，对于由缺失而非随机机制生成的缺失数据，假设MAR可能不令人满意，进行MI。对于给定数据集中的不完整变量，其相应的总体边际分布也可能在外部数据源中提供。我们展示了如何在插补模型中容易地利用这些信息，通过纳入适当计算的偏移量（称为“校准-δ调整”）来校准对总体的推断。我们描述了从不完全变量的总体分布中推导出这种偏移量，并展示了在应用中如何使用它来密切（通常是精确）匹配插补后分布与总体水平。通过分析和模拟研究，我们证明了当数据为MAR时，我们提出的校正-δ调整MI方法可以给出与标准MI相同的推断，并且在两种一般缺失机制下可以产生更准确的推断。该方法用于插补使用英国初级保健电子健康记录的2型糖尿病患病率病例研究中缺失的种族数据，与标准MI相比，该方法导致非白色种族组推断的科学相关变化。经校准的δ调整MI代表了一种实用方法，用于在敏感性分析中利用可用的人群水平信息，以探索MAR假设的潜在偏离。
Multiple imputation (MI) has become popular for analyses with missing data in medical research. The standard implementation of MI is based on the assumption of data being missing at random (MAR). However, for missing data generated by missing not at random mechanisms, MI performed assuming MAR might not be satisfactory. For an incomplete variable in a given data set, its corresponding population marginal distribution might also be available in an external data source. We show how this information can be readily utilised in the imputation model to calibrate inference to the population by incorporating an appropriately calculated offset termed the “calibrated‐δ adjustment.” We describe the derivation of this offset from the population distribution of the incomplete variable and show how, in applications, it can be used to closely (and often exactly) match the post‐imputation distribution to the population level. Through analytic and simulation studies, we show that our proposed calibrated‐δ adjustment MI method can give the same inference as standard MI when data are MAR, and can produce more accurate inference under two general missing not at random missingness mechanisms. The method is used to impute missing ethnicity data in a type 2 diabetes prevalence case study using UK primary care electronic health records, where it results in scientifically relevant changes in inference for non‐White ethnic groups compared with standard MI. Calibrated‐δ adjustment MI represents a pragmatic approach for utilising available population‐level information in a sensitivity analysis to explore potential departures from the MAR assumption.
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通讯作者：
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