Zhao Jiwei, Chen Chi
Department of Biostatistics, State University of New York at Buffalo.
J Nonparametr Stat. 2019;31(4):911-931. doi: 10.1080/10485252.2019.1664739. Epub 2019 Sep 18.
Nonignorable missing-data is common in studies where the outcome is relevant to the subject's behavior. Ibrahim et al. (2001) fitted a logistic regression for a binary outcome subject to nonignorable missing data, and they proposed to replace the outcome in the mechanism model with an auxiliary variable that is completely observed. They had to correctly specify a model for the auxiliary variable; unfortunately the outcome variable subject to nonignorable missingness is still involved. The correct specification of this model is mysterious. Instead, we propose two unconventional likelihood based estimation procedures where the nonignorable missingness mechanism model could be completely bypassed. We apply our proposed methods to the children's mental health study and compare their performance with existing methods. The large sample properties of the proposed estimators are rigorously justified, and their finite sample behaviors are examined via comprehensive simulation studies.
在研究中，当结果与受试者的行为相关时，不可忽略的缺失数据很常见。易卜拉欣等人（2001年）针对存在不可忽略缺失数据的二元结果拟合了逻辑回归模型，他们建议用一个完全可观测的辅助变量来替代机制模型中的结果。他们必须正确指定辅助变量的模型；不幸的是，仍然涉及存在不可忽略缺失性的结果变量。这个模型的正确指定很神秘。相反，我们提出了两种基于非常规似然的估计程序，其中可以完全绕过不可忽略缺失机制模型。我们将所提出的方法应用于儿童心理健康研究，并将其性能与现有方法进行比较。对所提出估计量的大样本性质进行了严格论证，并通过全面的模拟研究检验了它们的有限样本行为。
