IRT Modeling in the Presence of Zero-Inflation With Application to Psychiatric Disorder Severity
IRT Modeling in the Presence of Zero-Inflation With Application to Psychiatric Disorder Severity
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零通货膨胀情况下的 IRT 建模及其在精神疾病严重程度中的应用
DOI：
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发表时间：
2015
影响因子：
1.2
通讯作者：
I. Moustaki
中科院分区：
文献类型：
--
作者：
M. Wall;Jung Yeon Park;I. Moustaki
项目反应理论（IRT）已越来越多地用于精神病学的目的，描述项目之间的关系，在精神疾病症状电池假设是一个潜在的潜在的连续性状的指标，代表了精神疾病的严重程度。通常会发现零膨胀（ZI）数据，使得大部分样本没有任何症状。有人认为，精神障碍症状的标准IRT模型可能是有问题的，由于许多临床特征的单极性。在本文中，作者提出通过使用混合模型来近似IRT模型中未知的潜在特质分布，同时允许存在非病理性亚组来解决这个问题。其基本思想是，与假设潜在特质的正态性不同，潜在特质将被允许遵循包括退化分量的正态混合，该退化分量被固定以代表非病理组，对该非病理组而言，精神症状根本不相关，因此预期全部为零。作者演示了如何在Mplus中实现ZI混合IRT方法，并提出了一个模拟研究，将其性能与标准IRT模型进行比较，假设在不同情况下代表精神障碍症状电池的正态性。该模型不正确地假设正态性，显示有偏见的歧视和严重程度的估计。一个应用程序进一步说明了使用来自酒精使用障碍标准组的数据的方法。
Item response theory (IRT) has been increasingly utilized in psychiatry for the purpose of describing the relationship among items in psychiatric disorder symptom batteries hypothesized to be indicators of an underlying latent continuous trait representing the severity of the psychiatric disorder. It is common to find zero-inflated (ZI) data such that a large proportion of the sample has none of the symptoms. It has been argued that standard IRT models of psychiatric disorder symptoms may be problematic due to the unipolar nature of many clinical traits. In the current article, the authors propose to address this by using a mixture model to approximate the unknown latent trait distribution in the IRT model while allowing for the presence of a non-pathological subgroup. The basic idea is that instead of assuming normality for the underlying trait, the latent trait will be allowed to follow a mixture of normals including a degenerate component that is fixed to represent a non-pathological group for whom the psychiatric symptoms simply are not relevant and hence are all expected to be zero. The authors demonstrate how the ZI mixture IRT method can be implemented in Mplus and present a simulation study comparing its performance with a standard IRT model assuming normality under different scenarios representative of psychiatric disorder symptom batteries. The model incorrectly assuming normality is shown to have biased discrimination and severity estimates. An application further illustrates the method using data from an alcohol use disorder criteria battery.
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作者：
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影响因子：
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