Issues regarding missing data are critical in observational and experimental research. Recently, for datasets with mixed continuous–discrete variables, multiple imputation by chained equation (MICE) has been widely used, although MICE may yield severely biased estimates. We propose a new semiparametric Bayes multiple imputation approach that can deal with continuous and discrete variables. This enables us to overcome the shortcomings of MICE; they must satisfy strong conditions (known as compatibility) to guarantee obtained estimators are consistent. Our simulation studies show the coverage probability of 95% interval calculated using MICE can be less than 1%, while the MSE of the proposed can be less than one-fiftieth. We applied our method to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and the results are consistent with those of the previous works that used panel data other than ADNI database, whereas the existing methods, such as MICE, resulted in inconsistent results.

中文翻译：

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缺失混合连续离散协变量的回归模型的半参数贝叶斯多重插补

有关缺失数据的问题在观察和实验研究中至关重要。最近，对于具有混合连续离散变量的数据集，链式方程多重插补 (MICE) 已被广泛使用，尽管 MICE 可能会产生严重偏差的估计。我们提出了一种新的半参数贝叶斯多重插补方法，可以处理连续和离散变量。这使我们能够克服MICE的缺点；它们必须满足强条件（称为兼容性）以保证获得的估计量是一致的。我们的模拟研究表明，使用 MICE 计算的 95% 区间的覆盖概率可以小于 1%，而所提出的 MSE 可以小于五十分之一。我们将我们的方法应用于阿尔茨海默病神经影像学倡议 (ADNI) 数据集，