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Modeling and treating internalizing psychopathology in a clinical trial: a latent variable structural equation modeling approach

Published online by Cambridge University Press:  09 January 2013

M. G. Kushner ,
R. F. Krueger ,
M. M. Wall ,
E. W. Maurer ,
J. S. Menk  and
K. R. Menary
M. G. Kushner*
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
R. F. Krueger
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
M. M. Wall
Affiliation:
Departments of Psychiatry and Biostatistics, Columbia University, New York City, NY, USA
E. W. Maurer
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
J. S. Menk
Affiliation:
Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
K. R. Menary
Affiliation:
Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
*
*Address for correspondence: M. G. Kushner, Ph.D., 282-2A West, 2450 Riverside Avenue, Minneapolis, MN 55454, USA. (Email: kushn001@umn.edu)
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Abstract

Background

Clinical trials are typically designed to test the effect of a specific treatment on a single diagnostic entity. However, because common internalizing disorders are highly correlated (‘co-morbid’), we sought to establish a practical and parsimonious method to characterize and quantify changes in a broad spectrum of internalizing psychopathology targeted for treatment in a clinical trial contrasting two transdiagnostic psychosocial interventions.

Method

Alcohol dependence treatment patients who had any of several common internalizing disorders were randomized to a six-session cognitive-behavioral therapy (CBT) experimental treatment condition or a progressive muscle relaxation training (PMRT) comparison treatment condition. Internalizing psychopathology was characterized at baseline and 4 months following treatment in terms of the latent structure of six distinct internalizing symptom domain surveys.

Results

Exploratory structural equation modeling (ESEM) identified a two-factor solution at both baseline and the 4-month follow-up: Distress (measures of depression, trait anxiety and worry) and Fear (measures of panic anxiety, social anxiety and agoraphobia). Although confirmatory factor analysis (CFA) demonstrated measurement invariance between the time-points, structural models showed that the latent means of Fear and Distress decreased substantially from baseline to follow-up for both groups, with a small but statistically significant advantage for the CBT group in terms of Distress (but not Fear) reduction.

Conclusions

The approach demonstrated in this study provides a practical solution to modeling co-morbidity in a clinical trial and is consistent with converging evidence pointing to the dimensional structure of internalizing psychopathology.

Keywords

Anxiety disorderclinical trialinternalizinglatent variable structural equation modelingpsychopathology
Type
Original Articles
Information
Psychological Medicine , Volume 43 , Issue 8 , August 2013 , pp. 1611 - 1623
Copyright
Copyright © Cambridge University Press 2013 

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