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Missing data methods for arbitrary missingness with small samples

机译：带有小样本的任意缺失的缺失数据方法

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摘要

Missing data are a prevalent and widespread data analytic issue and previous studies have performed simulations to compare the performance of missing data methods in various contexts and for various models; however, one such context that has yet to receive much attention in the literature is the handling of missing data with small samples, particularly when the missingness is arbitrary. Prior studies have either compared methods for small samples with monotone missingness commonly found in longitudinal studies or have investigated the performance of a single method to handle arbitrary missingness with small samples but studies have yet to compare the relative performance of commonly implemented missing data methods for small samples with arbitrary missingness. This study conducts a simulation study to compare and assess the small sample performance of maximum likelihood, listwise deletion, joint multiple imputation, and fully conditional specification multiple imputation for a single-level regression model with a continuous outcome. Results showed that, provided assumptions are met, joint multiple imputation unanimously performed best of the methods examined in the conditions under study.
机译：丢失数据是一个普遍且广泛的数据分析问题，以前的研究已经进行了仿真，以比较各种情况下和各种模型下丢失数据方法的性能。然而，在文献中尚未引起广泛关注的一种情况是使用小样本处理缺失数据，尤其是在缺失是任意的情况下。先前的研究或者比较了纵向研究中常见的单样本缺失的小样本方法，还是研究了处理小样本任意缺失的单一方法的性能，但是研究尚未针对小样本常用的缺失数据方法的相对性能进行比较。具有任意缺失的样本。这项研究进行了模拟研究，以比较和评估具有连续结果的单级回归模型的最大似然，按列表删除，联合多次插补和完全条件指定多次插补的小样本性能。结果表明，在满足假设的前提下，联合多重插补在研究条件下一致地执行了最佳方法。

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