Cited by 55Psychotherapy Techniques and ApplicationsMental Health Treatment and AccessAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations

Zilcha-Mano, Sigal, Keefe, John R., Chui, Harold, Rubin, Avinadav, Barrett, Marna S., Barber, Jacques P.·2016·The Journal of Clinical Psychiatry

Abstract

OBJECTIVE: Premature discontinuation of therapy is a widespread problem that hampers the delivery of mental health treatment. A high degree of variability has been found among rates of premature treatment discontinuation, suggesting that rates may differ depending on potential moderators. In the current study, our aim was to identify demographic and interpersonal variables that moderate the association between treatment assignment and dropout. METHODS: Data from a randomized controlled trial conducted from November 2001 through June 2007 (N = 156) comparing supportive-expressive therapy, antidepressant medication, and placebo for the treatment of depression (based on DSM-IV criteria) were used. Twenty prerandomization variables were chosen based on previous literature. These variables were subjected to exploratory bootstrapped variable selection and included in the logistic regression models if they passed variable selection. RESULTS: Three variables were found to moderate the association between treatment assignment and dropout: age, pretreatment therapeutic alliance expectations, and the presence of vindictive tendencies in interpersonal relationships. When patients were divided into those randomly assigned to their optimal treatment and those assigned to their least optimal treatment, dropout rates in the optimal treatment group (24.4%) were significantly lower than those in the least optimal treatment group (47.4%; P = .03). CONCLUSIONS: Present findings suggest that a patient's age and pretreatment interpersonal characteristics predict the association between common depression treatments and dropout rate. If validated by further studies, these characteristics can assist in reducing dropout through targeted treatment assignment. TRIAL REGISTRATION: Secondary analysis of data from ClinicalTrials.gov identifier: NCT00043550.

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Crossref · 10.4088/jcp.15m10081

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Authors
Zilcha-Mano, S., Keefe, J. R., Chui, H., Rubin, A., Barrett, M. S., & Barber, J. P.
Affiliations
  • University of Pennsylvania (US)
  • Adelphi University (US)
  • University of Haifa (IL)
Venue
The Journal of Clinical Psychiatry
Published
2016-12-28
Volume
77
Issue
12
Pages
e1584-e1590
ISSN
0160-6689 (ISSN-L)
OpenAlex
W2562140166
Cited by
55
References
48
Citations by year
2026: 1 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 4 · 2020: 20 · 2019: 5 · 2018: 8 · 2017: 2 · 2016: 1
FWCI
7.13
Type
journal article
Language
en
Open access
closed
Locations
  • The Journal of Clinical Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
treatment discontinuation, depression, premature treatment discontinuation, treatment moderators, age, supportive-expressive therapy, antidepressants, placebo, treatment allocation
MeSH terms
Adult, Age Factors, Antidepressive Agents, Anxiety Disorders, Depressive Disorder, Female, Humans, Interpersonal Relations, Male, Middle Aged, Outcome and Process Assessment, Health Care, Patient Dropouts, Personality Disorders, Psychotherapy, Comorbidity, Young Adult

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