ArticleScientific reports2018
Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: A methodological proof-of-concept study.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- A systematic review of studies using network analysis to assess dynamics of psychotic-like experiences in community samples.Psychological medicine · 2025Pooled it
- Predicting optimal treatment allocation for cognitive analytic-guided self-help versus cognitive behavioural-guided self-help.The British journal of clinical psychology · 2025Trial
- Longitudinal Network Analysis Reveals Interactive Change of Schizophrenia Symptoms During Acute Antipsychotic Treatment.Schizophrenia bulletin · 2023Trial
- The Nature, Timing, and Symptom Trajectories of Dropout From Transdiagnostic and Single-Diagnosis Cognitive-Behavioral Therapy for Anxiety Disorders.Behavior therapy · 2021Trial
- Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- Network Analysis Indicates That Avolition Is the Most Central Domain for the Successful Treatment of Negative Symptoms: Evidence From the Roluperidone Randomized Clinical Trial.Schizophrenia bulletin · 2020Trial
- Predicting Dropout in Psychotherapy for Major Depressive Disorder: A Machine Learning Approach to Identifying At-Risk Patients.Clinical psychology & psychotherapyTrial
- Network dynamics of depression, anxiety, sleep disturbances, and suicidal symptoms in Chinese adolescents: a longitudinal cross-sectional and cross-lagged panel network analysis.Psychological medicine · 2026Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Individual-Specific Animated Profiles of Mental Health.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Dynamic time warp versus vector autoregression models for network analyses of psychological processes.Scientific reports · 2025Article
- The Dialectical Relationship Between Burnout and Work Engagement: A Network Approach.Stress and health : journal of the International Society for the Investigation of Stress · 2025Article
- Commentary on Serre et al. : Demonstrating the next era of addiction science.Addiction (Abingdon, England) · 2025Article
- To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project.Administration and policy in mental health · 2025Article
- Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- The Network Structure of Relationship Obsessive-Compulsive Disorder Presentations: The Interplay between ROCD Symptoms with Maladaptive and Non-Maladaptive Personality Traits.The Psychiatric quarterly · 2024Article
- Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.Administration and policy in mental health · 2024Article
- Implementation of an Ecological Momentary Assessment (EMA) in Naturalistic Psychotherapy Settings: Qualitative Insights from Patients, Therapists, and Supervisors Perspectives.Administration and policy in mental health · 2024Article
- The Connections Matter: Bi-Directional Learning in Program Evaluation and Practice-Oriented Research.Administration and policy in mental health · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients' dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.