Predicting Psychotherapy Dropouts: A Multilevel Approach

Kegel, Alexander F.; Flückiger, Christoph

doi:10.1002/cpp.1899

Cited by 46 publications

(38 citation statements)

References 45 publications

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“…These include greater clinical severity (e.g., more complex clinical pictures, more comorbid diagnoses, poorer functioning, personality disorders; see Barrett et al, 2008, andSwift &Greenberg, 2012, for review), lower satisfaction with treatment and poorer patient-reported therapeutic alliance (Barrett et al, 2008;Kegel & Flückiger, 2014;Renk & Dinger, 2002;Shamir, Szor, & Melamed, 2010;Wnuk et al, 2013), and lower socioeconomic status and younger age (see Barrett et al, 2008;Swift & Greenberg, 2012 for review). Findings regarding sex, education level, race/ethnicity, and negative attitudes toward treatment have been equivocal (Anestis, Finn, Gottfried, Arbisi, & Joiner, 2015;Barrett et al, 2008;Chisholm, Crowther, & Ben-Porath, 1997;Gilmore, Lash, Foster, & Blosser, 2001;Munley & Busby, 1994;Swift & Greenberg, 2012).…”

Section: Journal Of Clinical Psychology Xxxx 2016mentioning

confidence: 98%

“…On this note, past work on treatment attrition among other clinical populations highlights predictors of dropout that may be relevant to suicidal individuals. These include greater clinical severity (e.g., more complex clinical pictures, more comorbid diagnoses, poorer functioning, personality disorders; see Barrett et al, 2008, andSwift &Greenberg, 2012, for review), lower satisfaction with treatment and poorer patient-reported therapeutic alliance (Barrett et al, 2008;Kegel & Flückiger, 2014;Renk & Dinger, 2002;Shamir, Szor, & Melamed, 2010;Wnuk et al, 2013), and lower socioeconomic status and younger age (see Barrett et al, 2008;Swift & Greenberg, 2012 for review).…”

Section: Treatment Attrition Suicidal Individuals 89mentioning

confidence: 99%

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Predictors of Treatment Attrition Among Adult Outpatients With Clinically Significant Suicidal Ideation

Hom

Joiner

2016

J Clin Psychol

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Section: Journal Of Clinical Psychology Xxxx 2016mentioning

confidence: 98%

Section: Treatment Attrition Suicidal Individuals 89mentioning

confidence: 99%

Predictors of Treatment Attrition Among Adult Outpatients With Clinically Significant Suicidal Ideation

Hom

Joiner

2016

J Clin Psychol

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“…After treatment discontinuation, remission is out of reach. Patients either transfer back to the depressed state or remit spontaneously (because treatment discontinuation is often associated with spontaneous or premature remission) [53,54]. In every state, each individual is at risk of all-cause mortality.…”

Section: Model Structurementioning

confidence: 99%

Cost–Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar Depression: A Markov Model Simulation

Baumann

Stargardt

Frey

2020

Appl Health Econ Health Policy

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Background and Objective Unipolar depression is the most common form of depression and demand for treatment, such as psychotherapy, is high. However, waiting times for psychotherapy often considerably exceed their recommended maximum. As a potentially less costly alternative treatment, internet-based cognitive behavior therapy (ICBT) might help reduce waiting times. We therefore analyzed the cost-utility of ICBT compared to face-to-face CBT (FCBT) as an active control treatment, taking differences in waiting time into account. Methods We constructed a Markov model to simulate costs and health outcomes measured in quality-adjusted life years (QALYs) for ICBT and FCBT in Germany. We modeled a time horizon of 3 years using six states (remission, depressed, spontaneous remission, undergoing treatment, treatment finished, death). The societal perspective was adopted. We obtained parameters for transition probabilities, depression-specific QoL, and cost data from the literature. Deterministic and probabilistic sensitivity analyses were conducted. Within a scenario analysis, we simulated different time-to-treatment combinations. Half-cycle correction was applied. Results In our simulation, ICBT generated 0.260 QALYs and saved €2536 per patient compared to FCBT. Our deterministic sensitivity analysis suggests that the base-case results were largely unaffected by parameter uncertainty and are therefore robust. Our probabilistic sensitivity analysis suggests that ICBT is highly likely to be more effective (91.5%), less costly (76.0%), and the dominant strategy (69.7%) compared to FCBT. The scenario analysis revealed that the base-case results are robust to variations in time-to-treatment differences. Conclusion ICBT has a strong potential to balance demand and supply of CBT in unipolar depression by reducing therapist time per patient. It is highly likely to generate more QALYs and reduce health care expenditure. In addition, ICBT may have further positive external effects, such as freeing up capacities for the most severely depressed patients.

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“… 3 Karyotaki et al (2015) Demographics Estimation of drop-out risk factors Hierarchical Poisson regression modeling Individual Patient Data Meta-Analysis: raw data from various trials were analyzed to identify drop-out risk factors for web-based interventions. 3 Kegel and Flückiger (2014) Self-esteem, mastery, clarification, global Alliance Treatment dropout Hierarchical regression Clients with lower levels of self-esteem, fewer clarifying experiences, and absence of therapeutic alliance are more likely to dropout. 3 Meulenbeek et al (2015) Socio-demographic, personal, and illness-related variables Treatment dropout Logistic regression Dropout estimation for clients with mild panic disorder.…”

Section: Applying the Framework To Published Researchmentioning

confidence: 99%

“…Using regression techniques, clinical researchers have been exploring Type 3 models, using predictors that were collected at baseline (pre-test) assessments (e.g., DeRubeis et al, 2014 ; Huibers et al, 2015 ; Karyotaki et al, 2015 ; Kegel and Flückiger, 2014 ; Meulenbeek et al, 2015 ), mental health questionnaire responses that were collected during treatment (e.g., Proudfoot et al, 2013 ; Van et al, 2008 ), or repeated measures of the therapeutic relationship between the patient and the therapists (e.g., Priebe et al, 2011 ). In these studies, researchers tend to focus more on the importance of individual predictors on a population level (i.e., risk factors), rather than on the predictive power of the model as a whole.…”

Section: Applying the Framework To Published Researchmentioning

confidence: 99%

Predictive modeling in e-mental health: A common language framework

Becker

Breda

Funk

et al. 2018

Internet Interventions

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Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

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Predicting Psychotherapy Dropouts: A Multilevel Approach

Cited by 46 publications

References 45 publications

Predictors of Treatment Attrition Among Adult Outpatients With Clinically Significant Suicidal Ideation

Predictors of Treatment Attrition Among Adult Outpatients With Clinically Significant Suicidal Ideation

Cost–Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar Depression: A Markov Model Simulation

Predictive modeling in e-mental health: A common language framework

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