Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations
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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An important component of patient-centered care is associated with more promising patient outcomes.
“This is an important component of patient-centered care and is associated with more promising patient outcomes [27,28].”
From Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care (2023) · cites this paper as [28]
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Self-citationKappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M., Klein, D. N., Arnow, B. A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12432323
Self-citationKappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M., Klein, D. N., Arnow, B. A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12432308
Self-citationKappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M., Klein, D. N., Arnow, B. A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12432317
Self-citationKappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M., Klein, D. N., Arnow, B. A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12432281
Self-citationKappelmann, N., Rein, M., Fietz, J., Mayberg, H. S., Craighead, W. E., Dunlop, B. W., Nemeroff, C. B., Keller, M., Klein, D. N., Arnow, B. A., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12432338
Self-citationPeerbooms, V., van den Boogaard, T. M., Wieser, M. J., & van der Heiden, C. (2026) · Clinical Psychology in Europe · doi:10.32872/cpe.17335
- 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
- Publisher
- Physicians Postgraduate Press, Inc
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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