Abstract

The difference between two binomial proportions is commonly used in applied research. Since many studies encounter incomplete data, proper methods to analyze such data are needed. Here, we present a proper multiple imputation (MI) procedure for constructing confidence interval for difference between binomial proportions using Newcombe’s method, which is known to have a better coverage probability when compared with Wald’s method. We use both a conventional MI procedure for ignorable missingness and a two-stage MI for non-ignorable missingness. Using simulation studies, we compare our method to three other methods and provide recommendation for the use of such methods in practice. In addition, we show the application of our new method on a COVID-19 dataset.

摘要

两个二项式比例之间的差异通常用于应用研究。由于许多研究遇到不完整的数据，因此需要适当的方法来分析这些数据。在这里，我们提出了一个适当的多重插补 (MI) 程序，用于使用 Newcombe 方法构建二项式比例之间差异的置信区间，与 Wald 方法相比，已知该方法具有更好的覆盖概率。我们使用传统的 MI 程序来处理可忽略的缺失，并使用两阶段 MI 来处理不可忽略的缺失。通过模拟研究，我们将我们的方法与其他三种方法进行比较，并为在实践中使用这些方法提供建议。此外，我们展示了我们的新方法在 COVID-19 数据集上的应用。