A taxometric analysis of panic disorder.

Cogn Behav Ther

Department of Psychology, Appalachian State University, Boone, NC, USA.

Published: November 2024

AI Article Synopsis

  • Panic disorder is expensive to manage, and though there are effective treatments, getting a diagnosis often limits access to them.
  • Current diagnostic methods classify panic disorder strictly as either having it or not, which contradicts research suggesting a more nuanced, dimensional approach.
  • Recent studies using taxometric analyses on data from 663 participants indicate strong support for a dimensional model of panic disorder, which could improve how we measure, diagnose, and treat the condition.

Article Abstract

Panic disorder is costly, and while evidence-based interventions for panic disorder are effective, obtaining a diagnosis often precludes access to such treatments. This is problematic because the categorical diagnosis of panic disorder (i.e. "you have it, or you don't") supported by modern diagnostic manuals contradicts empirically supported dimensional models of panic disorder. Taxometric analyses, which test the dimensional or categorical latent structure of constructs, have consistently revealed dimensional latent structures when applied to other anxiety disorders and panic-related processes, but taxometric analyses have never been applied to panic disorder. To address this gap in the literature, three nonredundant taxometric procedures were applied to seven theoretically-relevant indicators of panic disorder derived from Panic Disorder Severity Scale data collected from 663 participants recruited via Amazon Mechanical Turk. Simulated comparison plots and objective fit indices were also evaluated. The collective results provided consistent empirical support for a dimensional model of panic disorder, with an overall mean CCFI score of .39. The implications of the present findings for the measurement, assessment, diagnosis, and treatment of panic disorder are discussed.

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http://dx.doi.org/10.1080/16506073.2024.2423656DOI Listing

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