Measuring anxiety in patients with early-stage Parkinson's disease: Rasch analysis of the state-trait anxiety inventory

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The State-Trait Anxiety Inventory (STAI), composed of two 20-item subscales (STAI-state and STAI-trait), has been increasingly used to assess anxiety symptoms in patients with Parkinson's disease (PD). However, the clinimetric attributes of the STAI under the statistical framework of the item-response theory (IRT) have not been fully elucidated within this population to date. We performed an IRT-based Rasch analysis of the STAI outcomes of patients with de novo PD from the Parkinson's Progression Markers Initiative database. The unidimensionality, Rasch model fit, scale targeting, separation reliability, differential item functioning, and response category utility of the STAI were statistically evaluated. A total of 326 (209 males, 117 females) patients without cognitive dysfunction were enrolled in our study. The original versions of the STAI-state and STAI-trait had acceptable separation reliability but lacked appropriate response category functioning, exhibited scale off-targeting, and several items demonstrated poor fit to the Rasch model. The response categories were reduced from four to three, and the rescored three-point TASI-trait demonstrated a marked improvement in clinimetric properties without a significant impact on unidimensionality and separation reliability. The rescored three-point version of the STAI-state required the additional removal of four misfitting items in order to improve the Rasch model fit. To our knowledge, this is the first study to assess the measurement properties based on the IRT of the STAI in patients with PD. Our Rasch analysis identified the components requiring possible amendments in order to improve the clinimetric attributes of the STAI.




Yang, H. J., Ahn, J. H., Lee, J., Lee, W. K., Lee, J., & Kim, Y. (2019). Measuring anxiety in patients with early-stage Parkinson’s disease: Rasch analysis of the state-trait anxiety inventory. Frontiers in Neurology, 10(FEB).

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