Identification problem of transition models for repeated measurement data with nonignorable missing
Identification problem of transition models for repeated measurement data with nonignorable missing
复制标题
不可忽略缺失重复测量数据转移模型识别问题
DOI：
10.1016/j.jmva.2017.12.007
复制
发表时间：
2018
影响因子：
1.6
通讯作者：
Y.
中科院分区：
文献类型：
--
作者：
Morikawa;K. and Kano;Y.
本文考虑了一个描述重复测量数据的响应变量的转移模型，并给出了分析具有不可重复缺失值的数据时检验模型可辨识性的充分条件。充分条件可以给我们直观的模型特征，以实现可辨识性。除了对响应变量的模型假设外，还经常假设缺失数据机制的参数模型。在这篇文章中，我们考虑了两种情况下的可识别性：（i）响应变量分布和缺失数据机制都是参数的;（ii）其中之一是非参数的，即，全局模型是半参数的。在此基础上提出了实用的识别模型。我们还提出了一个应用程序的数据的比较试验的两个剂量的贮库醋酸甲羟孕酮。
In this paper, we consider a transition model on a response variable to describe repeated measurement data and we provide sufficient conditions to check model identifiability when analyzing data with nonignorable missing values. The sufficient conditions can give us intuitive model characteristics to achieve identifiability. In addition to the model assumptions on the response variable, a parametric model of the missing-data mechanism is often assumed. In this article, we consider identifiability in two situations: (i) both the response variable distribution and the missing-data mechanism are parametric; (ii) one of them is nonparametric, i.e., the global model is semiparametric. Useful identifiable models are proposed on the basis of these conditions. We also present an application to data of a comparative trial of two dosages of depot medroxyprogesterone acetate.