IRT modeling in the presence of zero-inflation with application to psychiatric disorder severity

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 representing the severity of the psychiatric disorder. It is common to find zero-inflated 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 paper we propose to address this by using a mixture model to approximate the unknown latent trait distribution in the item response theory model while at the same time allowing for the presence of a nonpathological subgroup. The basic idea is that instead of assuming normality for the underlying trait, we will allow the latent trait to follow a mixture of normals including a degenerate component which is fixed to represent a non-pathological group for whom the psychiatric symptoms simply are not relevant and hence are expected to all be zero. We demonstrate how the zero-inflated mixture IRT method can be implemented in Mplus and present a simulation study comparing its performance to 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.