Wilson Confidence Intervals for Binomial Proportions With Multiple Imputation for Missing Data

We present a Wilson interval for binomial proportions for use with multiple imputation for missing data. Using simulation studies, we show that it can have better repeated sampling properties than the usual confidence interval for binomial proportions based on Rubin’s combining rules. Further, in contrast to the usual multiple imputation confidence interval for proportions, the multiple imputation Wilson interval is always bounded by zero and one. Supplementary material is available online.

我们提出了一种适用于缺失数据多重插补（multiple imputation）的二项分布比例威尔逊区间（Wilson interval）。通过模拟实验，我们证明相较于基于鲁宾合并法则（Rubin’s combining rules）的常规二项分布比例置信区间，该区间拥有更优的重复抽样性质。此外，与常规的比例多重插补置信区间不同，该多重插补威尔逊区间的取值范围始终介于0与1之间。在线可获取补充材料。