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首页> 外文期刊>Journal of applied statistics >A skew-normal random effects model for longitudinal ordinal categorical responses with missing data
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A skew-normal random effects model for longitudinal ordinal categorical responses with missing data

机译：缺失数据的纵向有序分类响应的偏态正态随机效应模型

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摘要

Missing values are common in longitudinal data studies. The missing data mechanism is termed non-ignorable (NI) if the probability of missingness depends on the non-response (missing) observations. This paper presents a model for the ordinal categorical longitudinal data with NI non-monotone missing values. We assumed two separate models for the response and missing procedure. The response is modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process. We employ these models in the context of so-called shared-parameter models, where the outcome and missing data models are connected by a common set of random effects. It is commonly assumed that the random effect follows the normal distribution in longitudinal data with or without missing data. This can be extremely restrictive in practice, and it may result in misleading statistical inferences. In this paper, we instead adopt a more flexible alternative distribution which is called the skew-normal distribution. The methodology is illustrated through an application to Schizophrenia Collaborative Study data and a simulation.
机译：缺失值在纵向数据研究中很常见。如果缺失的概率取决于无响应（缺失）的观测值，则将缺失数据机制称为不可忽略（NI）。本文提出了具有NI非单调缺失值的序数分类纵向数据模型。我们针对响应和缺失过程假设了两个单独的模型。响应被建模为顺序逻辑模型，而逻辑二进制模型被认为是缺少的过程。我们在所谓的共享参数模型的上下文中使用这些模型，其中结果和缺失数据模型通过一组常见的随机效应相连。通常假设随机效应遵循纵向数据中的正态分布，有或没有缺失数据。在实践中，这可能是非常严格的限制，并且可能导致误导性的统计推断。在本文中，我们改为采用更灵活的替代分布，即偏态正态分布。通过对精神分裂症协作研究数据和模拟的应用说明了该方法。

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