An integrated approach to test for missing not at random
An integrated approach to test for missing not at random
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发表时间：
2022-08
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通讯作者：
J. Noonan;A. A. Adediran-A.;R. Mitra;Stefanie Biedermann
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作者：
J. Noonan;A. A. Adediran-A.;R. Mitra;Stefanie Biedermann
缺失数据可能导致分析中的不确定性和偏倚，特别是当数据非随机缺失（MNAR）时。因此，理解和正确识别缺失数据机制至关重要。通过后续样本恢复缺失值允许研究人员对MNAR进行假设检验，这在仅使用原始不完整数据时是不可能的。调查这些测试的属性是如何通过后续的样本设计是很少探索的文献。我们的研究结果提供了一个这样的测试，基于常用的选择模型框架的属性全面的见解。我们确定了恢复样本的条件，使测试能够适当有效地应用，即具有已知的I型错误率并在功效方面进行了优化。因此，我们提供了一个综合的框架，用于测试MNAR的存在和设计后续样品在一个有效的成本效益的方式。我们的方法的性能进行评估，通过模拟研究，以及对一个真实的数据样本。
Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing not at random (MNAR). It is thus vital to understand and correctly identify the missing data mechanism. Recovering missing values through a follow up sample allows researchers to conduct hypothesis tests for MNAR, which are not possible when using only the original incomplete data. Investigating how properties of these tests are affected by the follow up sample design is little explored in the literature. Our results provide comprehensive insight into the properties of one such test, based on the commonly used selection model framework. We determine conditions for recovery samples that allow the test to be applied appropriately and effectively, i.e. with known Type I error rates and optimized with respect to power. We thus provide an integrated framework for testing for the presence of MNAR and designing follow up samples in an efficient cost-effective way. The performance of our methodology is evaluated through simulation studies as well as on a real data sample.