Web-Based Cognitive Behavioral Treatment for Bulimia Nervosa: A Randomized Clinical Trial.

Importance: Despite the existence of effective treatments, many individuals with bulimia nervosa (BN) do not receive evidence-based therapies. Integrating digital interventions into routine care might reach more patients and reduce the clinical burden of BN. Objective: To evaluate the effectiveness of a web-based cognitive behavioral self-help intervention for individuals with BN. Design, Setting, and Participants: A 2-group randomized clinical trial without follow-up was conducted between February 2, 2021, and July 9, 2022, in Germany. Participants aged between 18 and 65 years who met the diagnostic criteria for BN were enrolled online via self-referral. Data analyses were conducted from October 24, 2022, to December 23, 2023. Interventions: A web-based cognitive behavioral self-help intervention including 12 weekly modules was compared with a waiting-list control group only having access to routine care. Main Outcomes and Measures: The primary outcome was the change in the number of bulimic episodes between baseline and posttreatment. Secondary outcomes included changes in global eating disorder symptoms, clinical impairment, well-being, work capacity, comorbid symptoms, self-esteem, and emotion regulation complemented by weekly measures and ecological momentary assessment. Intention-to-treat analyses were performed. Results: Participants (N = 154; mean [SD] age, 29.6 [8.6] years; 149 [96.8%] female) receiving the web-based intervention demonstrated a significantly greater decrease in bulimic episodes compared with the control group (Cohen d = -0.48; 95% CI, -0.75 to -0.20; P < .001), representing a significant change in binge-eating episodes (Cohen d = -0.61; 95% CI, -0.89 to -0.33; P < .001), but not in compensatory behaviors (Cohen d = -0.25; 95% CI, -0.51 to 0.02; P = .21). The intervention was superior in improving global eating disorder symptoms (Cohen d = -0.61; 95% CI, -0.89 to -0.32; P < .001) and clinical impairment (Cohen d = -0.62; 95% CI, -0.92 to -0.33; P < .001). No significant effects were found for well-being (Cohen d = -0.08; 95% CI, -0.37 to 0.22; P > .99) and work capacity (Cohen d = -0.01; 95% CI, -0.68 to 0.66; P = .99). Exploratory analyses indicated significant changes in self-esteem and emotion regulation difficulties, but not in comorbid symptoms. Conclusions and Relevance: In this randomized clinical trial, a web-based cognitive behavioral self-help intervention effectively decreased eating disorder symptoms and illness-related burden in individuals with BN, underlining the potential of digital interventions to complement established treatments. Trial Registration: ClinicalTrials.gov Identifier: NCT04876196.

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms.

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Love yourself as a therapist, doubt yourself as an institution? Therapist and institution effects on outcome, treatment length, and dropout.

OBJECTIVE: Research suggests that some therapists achieve better outcomes than others. However, an overlooked area of study is how institution differences impact patient outcomes independent of therapist variance. This study aimed to examine the role of institution and therapist differences in adult outpatient psychotherapy. METHOD: The study included 1428 patients who were treated by 196 therapists at 10 clinics. Two- and three-level hierarchical linear regression models were employed to investigate the effects of therapists and institutions on three dependent patient variables: (1) symptom change, (2) treatment duration, and (3) dropout. Level three explanatory variables were tested. RESULTS: The results showed that therapist effects (TE) were significant for all three types of treatment outcome (7.8%-18.2%). When a third level (institution) was added to the model, the differences between therapists decreased, and significant institution effects (IE) were found: 6.3% for symptom change, 10.6% for treatment duration, and 6.5% for dropout. The exploratory analyses found no predictors able to explain the systematic variation at the institution level. DISCUSSION: TE on psychotherapy outcomes remain a relevant factor but may have been overestimated in previous studies due to not properly distinguishing them from differences at the institution level.

Neural correlates of stress-reactive rumination in depression - The role of childhood trauma and social anxiety.

Recent work showed an association of prefrontal dysfunctions in patients with Major Depressive Disorder (MDD) and social stress induced rumination. However, up to date it is unclear which etiological features of MDD might cause prefrontal dysfunctions. In the study at hand, we aimed to replicate recent findings, that showed prefrontal activation alterations during the Trier Social Stress Test (TSST) and subsequently increased stress-reactive rumination in MDD compared to healthy controls. Moreover, we aimed to explore the role of adverse childhood experiences and other clinical variables in this relationship. N = 55 patients currently suffering from MDD and n = 42 healthy controls (HC) underwent the TSST, while cortical activity in areas of the Cognitive Control Network (CCN) was measured via functional near-infrared spectroscopy (fNIRS). The TSST successfully induced a stress reaction (physiologically, as well as indicated by subjective stress ratings) and state rumination in all subjects with moderate to large effect sizes. In comparison to HC, MDD patients showed elevated levels of state rumination with large effect sizes, as well as a typical pattern of reduced cortical oxygenation during stress in the CCN with moderate effect sizes. Self-reported emotional abuse and social anxiety were moderately positively associated with increased stress-reactive rumination. Within the MDD sample, emotional abuse was negatively and social anxiety positively associated with cortical oxygenation within the CCN with moderate to large effect sizes. In conclusion, our results replicate previous findings on MDD-associated prefrontal hypoactivity during stress and extends the research toward specific subtypes of depression.

Effectiveness of a Web-Based Cognitive Behavioral Self-Help Intervention for Binge Eating Disorder: A Randomized Clinical Trial.

Importance: Binge eating disorder (BED) is one of the most frequent eating pathologies and imposes substantial emotional and physical distress, yet insufficient health care resources limit access to specialized treatment. Web-based self-help interventions emerge as a promising solution, offering more accessible care. Objective: To examine the effectiveness of a web-based cognitive behavioral self-help intervention for individuals with BED. Design, Setting, and Participants: This 2-arm, parallel-group randomized clinical trial conducted from January 15, 2021, to August 3, 2022, in Germany and other German-speaking countries enrolled patients aged 18 to 65 years who met the diagnostic criteria for BED (according to the Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]). Data analysis occurred between January 27 and September 4, 2023, following our statistical analysis plan. Interventions: Participants were randomized to a web-based self-help intervention or a waiting-list control condition. Main Outcomes and Measures: The primary outcome was a change in objective binge eating episodes from baseline to after treatment. Secondary outcomes included global eating pathology, clinical impairment, work capacity, well-being, comorbid psychopathology, self-esteem, and emotion regulation. Results: A total of 1602 patients were screened, of whom 154 (mean [SD] age, 35.93 [10.59] years; 148 female [96.10%]) fulfilled the criteria for BED and were randomized (77 each to the intervention and control groups). The web-based intervention led to significant improvements in binge eating episodes (Cohen d, -0.79 [95% CI, -1.17 to -0.42]; P < .001), global eating psychopathology (Cohen d, -0.71 [95% CI, -1.07 to -0.35]; P < .001), weekly binge eating (Cohen d, -0.49 [95% CI, -0.74 to -0.24]; P < .001), clinical impairment (Cohen d, -0.75 [95% CI, -1.13 to -0.37]; P < .001), well-being (Cohen d, 0.38 [95% CI, 0.01 to 0.75]; P = .047), depression (Cohen d, -0.49 [95% CI, -0.86 to -0.12]; P = .01), anxiety (Cohen d, -0.37 [95% CI, -0.67 to -0.07]; P = .02), self-esteem (Cohen d, 0.36 [95% CI, 0.13 to 0.59]; P = .003), and emotion regulation (difficulties: Cohen d, -0.36 [95% CI, -0.65 to -0.07]; P = .01 and repertoire: Cohen d, 0.52 [95% CI, 0.19 to 0.84]; P = .003). Conclusion and Relevance: In this randomized clinical trial of a web-based self-help intervention for patients with BED, the findings confirmed its effectiveness in reducing binge eating episodes and improving various mental health outcomes, highlighting a scalable solution to bridge the treatment gap for this condition. Trial Registration: ClinicalTrials.gov Identifier: NCT04876183.

The relationship between interpersonal problems and therapeutic alliance in psychotherapy: A three-level mixed-effects meta-analysis.

Psychotherapy is an interpersonal process of collaboration toward specified treatment goals. The therapeutic alliance is well established as an important factor of psychotherapeutic change. However, the experience of distress in social interactions, commonly referred to as interpersonal problems, might be interfering with the collaborative process during psychotherapy. This study systematically reviews the literature and obtains an estimate of the relationship between pretreatment interpersonal problems and the quality of the therapeutic alliance. Overall, 27 studies with 48 correlation coefficients were included in the final analysis. Due to the nested structure of the data, a three-level meta-analytic approach with a restricted maximum likelihood estimator was applied. Alliance assessment phase, alliance rater, alliance measure instrument, and treatment type were tested as potential moderators. Heterogeneity and publication bias test were performed. The meta-analysis showed a small, but significant negative relationship between interpersonal problems at the beginning of psychotherapy and subsequent therapeutic alliance (r = -.12, SE = .02, 95% CI [-.16, -.08], p < .001, d = -.27). Only alliance assessment phase accounted for significant variability. There were no indications for a substantial publication bias. Interpersonal problems of patients before psychotherapy are a robust predictor for lower therapeutic alliance quality, albeit a small effect size. Consequently, patients who experience interpersonal problems may face greater challenges in developing a strong alliance with their therapists, especially in early stages of the treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using Progress Feedback to Enhance Treatment Outcomes: A Narrative Review.

We face increasing demand for greater access to effective routine mental health services, including telehealth. However, treatment outcomes in routine clinical practice are only about half the size of those reported in controlled trials. Progress feedback, defined as the ongoing monitoring of patients' treatment response with standardized measures, is an evidence-based practice that continues to be under-utilized in routine care. The aim of the current review is to provide a summary of the current evidence base for the use of progress feedback, its mechanisms of action and considerations for successful implementation. We reviewed ten available meta-analyses, which report small to medium overall effect sizes. The results suggest that adding feedback to a wide range of psychological and psychiatric interventions (ranging from primary care to hospitalization and crisis care) tends to enhance the effectiveness of these interventions. The strongest evidence is for patients with common mental health problems compared to those with very severe disorders. Effect sizes for not-on-track cases, a subgroup of cases that are not progressing well, are found to be somewhat stronger, especially when clinical support tools are added to the feedback. Systematic reviews and recent studies suggest potential mechanisms of action for progress feedback include focusing the clinician's attention, altering clinician expectations, providing new information, and enhancing patient-centered communication. Promising approaches to strengthen progress feedback interventions include advanced systems with signaling technology, clinical problem-solving tools, and a broader spectrum of outcome and progress measures. An overview of methodological and implementation challenges is provided, as well as suggestions for addressing these issues in future studies. We conclude that while feedback has modest effects, it is a small and affordable intervention that can potentially improve outcomes in psychological interventions. Further research into mechanisms of action and effective implementation strategies is needed.

What time-varying network models based on functional analysis tell us about the course of a patient's problem.

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling.

We aim to use topic modeling, an approach for discovering clusters of related words ("topics"), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ( r =0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ( r =0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach.

BACKGROUND: Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression. METHODS: Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test. RESULTS: Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients. CONCLUSION: The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Treatment credibility as a mechanism of change in cognitive behavioral therapy: Effects on depression and anxiety.

OBJECTIVE: This study aimed to provide evidence for treatment credibility (TC) as a potential mechanism of change in cognitive behavioral therapy (CBT). Therefore, it focused on within-person effects that are free of the influence of stable characteristics and thus allow to exclude certain alternative explanations for the association under study. METHOD: The sample included 1,423 patients receiving outpatient CBT, who presented a wide variety of psychiatric diagnoses (mostly affective and anxiety disorders). TC, depression, and anxiety were measured every fifth session from Session 5 to 25 using the Credibility Expectancy Questionnaire (CEQ), the Patient Health Questionnaire-9 (PHQ-9), and the General Anxiety Disorder-7 (GAD-7), respectively. Symptom severity was assessed every session using the Hopkins Symptom Checklist-11. Within- and between-person effects of TC, depression, and anxiety were analyzed using the latent curve model with structured residuals (LCM-SRs). In exploratory analyses, within-person effects of TC on next-session symptom severity were assessed using a modification of the LCM-SR. RESULTS: LCM-SRs exhibited excellent fit in main analyses. There were significant negative correlations of both intercepts and slopes (between-person level) of CEQ and PHQ-9 as well GAD-7. No significant cross-lagged effects (within-person level) were found over the five-session interval. However, session-wise analyses revealed significant cross-lagged effects of CEQ on Hopkins Symptom Checklist-11. CONCLUSIONS: This study is the first to find significant within-person effects of TC in session-wise analyses. This lends preliminary support to the notion of TC as a mechanism of change. The lack of significant findings at the five-session interval is discussed considering the specific design used in this study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project.

BACKGROUND: Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project. METHODS: In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis. RESULTS: Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities. CONCLUSIONS: This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Towards a taxonomy of mechanisms of change? Findings from an expert survey on the association between common factors and specific techniques in psychotherapy.

OBJECTIVE: In the present study, we used structural equation modeling (SEM) to investigate the complex relationship between common factors, i.e., mechanisms of change, and specific factors, i.e., therapeutic techniques. METHOD: N = 256 psychotherapy experts were asked to rate the appropriateness of 14 techniques commonly used in psychotherapy to facilitate five different common factors - resource activation, motivational clarification, self-management & emotion regulation, social competence, and therapeutic relationship. Using SEM, we defined techniques as indicators and common factors as latent variables. Data were split randomly into two subsets. Indicators were selected if three a priori defined criteria were met based on training data (n = 128). Subsequently, the goodness of model fit was assessed in the test data (n = 128). RESULTS: The proposed model revealed adequate fit. All factor loadings were theoretically sound and significant in magnitude. Findings suggest that psychotherapy experts discriminate between common factors by their various associations with therapeutic techniques. CONCLUSION: Suggestions are made, how therapeutic techniques are to be used to facilitate desirable change in the patient. Our model is a step towards a taxonomy of mechanisms of change that may help to improve research-informed decision-making.

Exploring mechanisms of change in the metacognitive training for depression.

The Metacognitive Training for Depression (D-MCT) is a highly structured group therapy that has been shown to be effective in reducing depressive symptoms. First evidence suggests that need for control represents a mechanism of change. However, more research is needed to evaluate the mode of action of each module and identify predictors of treatment response. Two sequential studies (one naturalistic pilot study [study I, N = 45] and one randomized controlled trial [study II, N = 32]) were conducted to evaluate the session-specific effects and predictors of D-MCT in patients with depression. The D-MCT was conducted over eight weeks, and patients answered a questionnaire on dysfunctional beliefs (e.g., negative filter) and depressive symptoms (e.g., lack of energy, self-esteem) before and after each session. Linear mixed-effects models showed that several dysfunctional beliefs and symptoms improved over the course of the treatment; three modules were able to evoke within-session effects, but no between-session effects were found. The improvement in lack of energy in one module was identified as a relevant predictor in study I via lasso regression but was not replicated in study II. Exploratory analyses revealed further predictors that warrant replication in future studies. The identified predictors were inconclusive when the two studies were compared, which may be explained by the different instruments administered. Even so, the results may be used to revise questionnaires and improve the intervention.

Can session-by-session changes in self-reported alliance scores serve as a measure of ruptures in the therapeutic alliance?

OBJECTIVE: During treatment, the therapeutic alliance is characterized by rupture and repair episodes, which in turn are associated with psychotherapy outcome. It would be important to have a parsimonious tool to identify ruptures in psychotherapy sessions to provide therapists with meaningful feedback about when they occur. The present study thus aims to establish whether measuring self-reported alliance dynamics can function as a measure of alliance ruptures. METHOD: The sample consisted of 58 depressed patients, who received 22 sessions of cognitive therapy for depression in an outpatient setting. The observer-rated Rupture Resolution Rating System (3RS) was applied to 58 sessions where the self-reported Working Alliance Inventory (WAI) completed by patients after each therapy session indicated that alliance ratings declined more than 2 SDs from that patient's individual mean. For comparison purposes, the 3RS was also applied to 58 randomly chosen sessions from the same treatment phase (early, middle, late). RESULTS: Results showed significant differences between sessions where the WAI indicated a drop in the alliance and randomly chosen sessions of the same treatment phase with regard to the frequency and impact of ruptures. CONCLUSION: This speaks for the construct validity of the 3RS. Session-by-session alliance ruptures may reliably be measured using a case-sensitive approach to identify meaningful drops in alliance self-report (WAI). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

OBJECTIVE: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. METHOD: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. RESULTS: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. CONCLUSION: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

To detrend, or not to detrend, that is the question? The effects of detrending on cross-lagged effects in panel models.

Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test the arguments for and against detrending CLPMs in the presence of an intervention effect. We conducted Monte Carlo simulations to examine the impact of trends on estimates of cross-lagged effects from several longitudinal structural equation models. Our simulations suggested that ignoring time trends led to biased estimates of auto- and cross-lagged effects in some conditions, while detrending did not introduce bias in any of the models. We used real data from an intervention study to illustrate how detrending may affect results. This example showed that models that separated trends from cross-lagged effects fit better to the data and showed nonsignificant effect of the mechanism on outcome, while models that ignored trends showed significant effects. We conclude that ignoring trends increases the risk of bias in estimates of auto- and cross-lagged parameters and may lead to spurious findings. Researchers can test for the presence of trends by comparing model fit of models that take into account individual differences in trends (e.g., autoregressive latent trajectory model, the latent curve model with structured residuals, or the general cross-lagged model). (PsycInfo Database Record (c) 2023 APA, all rights reserved).