Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

Which patients benefit from adding short-term psychodynamic psychotherapy to antidepressants in the treatment of depression? A systematic review and meta-analysis of individual participant data.

BACKGROUND: Adding short-term psychodynamic psychotherapy (STPP) to antidepressants increases treatment efficacy, but it is unclear which patients benefit specifically. This study examined efficacy moderators of combined treatment (STPP + antidepressants) v. antidepressants for adults with depression. METHODS: For this systematic review and meta-analysis (PROSPERO registration number: CRD42017056029), we searched PubMed, PsycINFO, Embase.com, and the Cochrane Library from inception to 1 January 2022. We included randomized clinical trials comparing combined treatment (antidepressants + individual outpatient STPP) v. antidepressants in the acute-phase treatment of depression in adults. Individual participant data were requested and analyzed combinedly using mixed-effects models (adding Cochrane risk of bias items as covariates) and an exploratory machine learning technique. The primary outcome was post-treatment depression symptom level. RESULTS: Data were obtained for all seven trials identified (100%, n = 482, combined: n = 238, antidepressants: n = 244). Adding STPP to antidepressants was more efficacious for patients with high rather than low baseline depression levels [B = -0.49, 95% confidence interval (CI) -0.61 to -0.37, p < 0.0001] and for patients with a depressive episode duration of >2 years rather than <1 year (B = -0.68, 95% CI -1.31 to -0.05, p = 0.03) and than 1-2 years (B = -0.86, 95% CI -1.66 to -0.06, p = 0.04). Heterogeneity was low. Effects were replicated in analyses controlling for risk of bias. CONCLUSIONS: To our knowledge, this is the first study that examines moderators across trials assessing the addition of STPP to antidepressants. These findings need validation but suggest that depression severity and episode duration are factors to consider when adding STPP to antidepressants and might contribute to personalizing treatment selection for depression.

Predictors and moderators of three online interventions for eating disorder symptoms in a randomized controlled trial.

OBJECTIVE: To optimize treatment recommendations for eating disorders, it is important to investigate whether some individuals may benefit more (or less) from certain treatments. The current study explored predictors and moderators of an automated online self-help intervention "Featback" and online support from a recovered expert patient. METHODS: Data were used from a randomized controlled trial. For a period of 8 weeks, participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, (2) chat or e-mail support from an expert patient, (3) Featback with expert-patient support, and (4) a waitlist. A mixed-effects partitioning method was used to see if age, educational level, BMI, motivation to change, treatment history, duration of eating disorder, number of binge eating episodes in the past month, eating disorder pathology, self-efficacy, anxiety and depression, social support, or self-esteem predicted or moderated intervention outcomes in terms of eating disorder symptoms (primary outcome), and symptoms of anxiety and depression (secondary outcome). RESULTS: Higher baseline social support predicted less eating disorder symptoms 8 weeks later, regardless of condition. No variables emerged as moderator for eating disorder symptoms. Participants in the three active conditions who had not received previous eating disorder treatment, experienced larger reductions in anxiety and depression symptoms. DISCUSSION: The investigated online low-threshold interventions were especially beneficial for treatment-naïve individuals, but only in terms of secondary outcomes, making them well-suited for early intervention. The study results also highlight the importance of a supportive environment for individuals with eating disorder symptoms. PUBLIC SIGNIFICANCE: To optimize treatment recommendations it is important to investigate what works for whom. For an internet-based intervention for eating disorders developed in the Netherlands, individuals who had never received eating disorder treatment seemed to benefit more from the intervention than those who had received eating disorder treatment, because they experienced larger reductions in symptoms of depression and anxiety. Stronger feelings of social support were related to less eating disorder symptoms in the future.

Translation, cultural adaptation and validity assessment of the Dutch version of the eHealth Literacy Questionnaire: a mixed-method approach.

BACKGROUND: The digitalization of healthcare requires users to have sufficient competence in using digital health technologies. In the Netherlands, as well as in other countries, there is a need for a comprehensive, person-centered assessment of eHealth literacy to understand and address eHealth literacy related needs, to improve equitable uptake and use of digital health technologies. OBJECTIVE: We aimed to translate and culturally adapt the original eHealth Literacy Questionnaire (eHLQ) to Dutch and to collect initial validity evidence. METHODS: The eHLQ was translated using a systematic approach with forward translation, an item intent matrix, back translation, and consensus meetings with the developer. A validity-driven and multi-study approach was used to collect validity evidence on 1) test content, 2) response processes and 3) internal structure. Cognitive interviews (n = 14) were held to assess test content and response processes (Study 1). A pre-final eHLQ version was completed by 1650 people participating in an eHealth study (Study 2). A seven-factor Confirmatory Factor Analysis (CFA) model was fitted to the data to assess the internal structure of the eHLQ. Invariance testing was performed across gender, age, education and current diagnosis. RESULTS: Cognitive interviews showed some problems in wording, phrasing and resonance with individual's world views. CFA demonstrated an equivalent internal structure to the hypothesized (original) eHLQ with acceptable fit indices. All items loaded substantially on their corresponding latent factors (range 0.51-0.81). The model was partially metric invariant across all subgroups. Comparison of scores between groups showed that people who were younger, higher educated and who had a current diagnosis generally scored higher across domains, however effect sizes were small. Data from both studies were triangulated, resulting in minor refinements to eight items and recommendations on use, score interpretation and reporting. CONCLUSION: The Dutch version of the eHLQ showed strong properties for assessing eHealth literacy in the Dutch context. While ongoing collection of validity evidence is recommended, the evidence presented indicate that the eHLQ can be used by researchers, eHealth developers and policy makers to identify eHealth literacy needs and inform the development of eHealth interventions to ensure that people with limited digital access and skills are not left behind.

Effectiveness of an online self-help program, expert-patient support, and their combination for eating disorders: Results from a randomized controlled trial.

OBJECTIVE: Many individuals with an eating disorder do not receive appropriate care. Low-threshold interventions could help bridge this treatment gap. The study aim was to evaluate the effectiveness of Featback, a fully automated online self-help intervention, online expert-patient support and their combination. METHOD: A randomized controlled trial with a 12-month follow-up period was conducted. Participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, a fully automated online self-help intervention, (2) chat or email support from a recovered expert patient, (3) Featback with expert-patient support and (4) a waiting list control condition. The intervention period was 8 weeks and there was a total of six online assessments. The main outcome constituted reduction of eating disorder symptoms over time. RESULTS: Three hundred fifty five participants, of whom 43% had never received eating disorder treatment, were randomized. The three active interventions were superior to a waitlist in reducing eating disorder symptoms (d = -0.38), with no significant difference in effectiveness between the three interventions. Participants in conditions with expert-patient support were more satisfied with the intervention. DISCUSSION: Internet-based self-help, expert-patient support and their combination were effective in reducing eating disorder symptoms compared to a waiting list control condition. Guidance improved satisfaction with the internet intervention but not its effectiveness. Low-threshold interventions such as Featback and expert-patient support can reduce eating disorder symptoms and reach the large group of underserved individuals, complementing existing forms of eating disorder treatment. PUBLIC SIGNIFICANCE STATEMENT: Individuals with eating-related problems who received (1) a fully automated internet-based intervention, (2) chat and e-mail support by a recovered individual or (3) their combination, experienced stronger reductions in eating disorder symptoms than those who received (4) usual care. Such brief and easy-access interventions play an important role in reaching individuals who are currently not reached by other forms of treatment.

The prevalence of stress-related outcomes and occupational well-being among emergency nurses in the Netherlands and the role of job factors: A regression tree analysis.

AIMS: This study aims to assess the prevalence of stress-related outcomes (burnout, sleep problems and post-traumatic stress) and occupational well-being (work engagement, job satisfaction and turnover intention) of Dutch emergency room nurses and to identify job factors related to key outcomes. BACKGROUND: While emergency nurses are prone to stress-related outcomes, no large-scale studies have been conducted in the Netherlands. Furthermore, few studies considered combined effects of job factors on emergency nurses' well-being. METHODS: In 2017, an occupation-specific survey was filled out by 701 (response: 74%) emergency nurses from 19 Dutch hospitals. Decision tree methods were used to identify the most important (combination of) job factors related to key outcomes. RESULTS: High prevalence of stress-related outcomes and turnover intention were found, while the majority experienced work engagement and were satisfied with their job. Emotional exhaustion was mainly associated with worktime demands and aggression/conflict situations. Work engagement was mainly associated with developmental opportunities. CONCLUSIONS: Dutch emergency room nurses are at risk of stress-related outcomes and have high turnover intention, while feeling engaged and satisfied with their job. IMPLICATIONS FOR NURSING MANAGEMENT: To retain and attract emergency room nurses, it is recommended to focus efforts on increasing developmental opportunities, while reducing worktime demands and aggression incidents.

Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data.

Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients, for example. This paper introduces GLMM trees, a decision-tree method for multilevel and longitudinal data. Method: To illustrate, we apply GLMM trees to a dataset of 3,256 young people (mean age 11.33, 48% girls) receiving treatment at one of several mental-health service providers in the UK. Two treatment outcomes (mental-health difficulties scores corrected for baseline) were regressed on 18 demographic, case and severity characteristics at baseline. We compared the performance of GLMM trees with that of traditional GLMMs and random forests. Results: GLMM trees yielded modest predictive accuracy, with cross-validated multiple R values of .18 and .25. Predictive accuracy did not differ significantly from that of traditional GLMMs and random forests, while GLMM trees required evaluation of a lower number of variables. Conclusion: GLMM trees provide a useful data-analytic tool for clinical prediction problems. The supplemental material provides a tutorial for replicating the GLMM tree analyses in R.

Predicting mental health improvement and deterioration in a large community sample of 11- to 13-year-olds.

Of children with mental health problems who access specialist help, 50% show reliable improvement on self-report measures at case closure and 10% reliable deterioration. To contextualise these figures it is necessary to consider rates of improvement for those in the general population. This study examined rates of reliable improvement/deterioration for children in a school sample over time. N = 9074 children (mean age 12; 52% female; 79% white) from 118 secondary schools across England provided self-report mental health (SDQ), quality of life and demographic data (age, ethnicity and free school meals (FSM) at baseline and 1 year and self-report data on access to mental health support at 1 year). Multinomial logistic regressions and classification trees were used to analyse the data. Of 2270 (25%) scoring above threshold for mental health problems at outset, 27% reliably improved and 9% reliably deteriorated at 1-year follow up. Of 6804 (75%) scoring below threshold, 4% reliably improved and 12% reliably deteriorated. Greater emotional difficulties at outset were associated with greater rates of reliable improvement for both groups (above threshold group: OR = 1.89, p < 0.001, 95% CI [1.64, 2.17], below threshold group: OR = 2.23, p < 0.001, 95% CI [1.93, 2.57]). For those above threshold, higher baseline quality of life was associated with greater likelihood of reliable improvement (OR = 1.28, p < 0.001, 95% CI [1.13, 1.46]), whilst being in receipt of FSM was associated with reduced likelihood of reliable improvement (OR = 0.68, p < 0.01, 95% CI [0.53, 0.88]). For the group below threshold, being female was associated with increased likelihood of reliable deterioration (OR = 1.20, p < 0.025, 95% CI [1.00, 1.42]), whereas being from a non-white ethnic background was associated with decreased likelihood of reliable deterioration (OR = 0.66, p < 0.001, 95% CI [0.54, 0.80]). For those above threshold, almost one in three children showed reliable improvement at 1 year. The extent of emotional difficulties at outset showed the highest associations with rates of reliable improvement.

Adolescent interpersonal relationships, social support and loneliness in high schools: Mediation effect and gender differences.

The purpose of this study was to explore the associations between the qualities of different types of relationships in school, social support and loneliness in adolescence. Using a sample (N=1674) of adolescent students randomly selected from middle schools, we found boys' loneliness was influenced by the qualities of opposite-sex, teacher-student and same-sex relationships, whereas girls' loneliness was only influenced by same-sex relationships. Additionally, social support mediated the association between same-sex relationships and teacher-student relationships, and loneliness. Further, the quality of same-sex relationships showed stronger association with boys' loneliness than girls'. Finally, the quality of same-sex relationships showed the strongest association with boys' loneliness comparing with opposite-sex relationships and teacher-student relationships. These findings are discussed to illuminate the possible mechanisms by which interpersonal relationships could influence loneliness. In future research, causal relationships and other influencing factors on loneliness should be examined.

The distribution of self-reported psychotic-like experiences in non-psychotic help-seeking mental health patients in the general population; a factor mixture analysis.

PURPOSE: Factor mixture analysis (FMA) and item response mixture models in the general population have shown that the psychosis phenotype has four classes. This study attempted to replicate this finding in help-seeking people accessing mental health services for symptoms of non-psychotic mental disorders. METHODS: All patients (18-35 years old) referred for non-psychotic mental health problems to the secondary mental healthcare service in The Hague between February 2008 to February 2010 (N = 3,694), were included. Patients completed the Prodromal Questionnaire (PQ). Hybrid latent class analysis was applied to explore the number, size and symptom profiles of the classes. RESULTS: The FMA resulted in four classes. Class 1 (N = 1,039, 28.1%) scored high on conceptual disorganization, inattention and mood disorder. Patients in Class 2 (N = 619, 16.8%) endorsed almost all PQ-items, were more often screened as being psychotic or at high risk of developing psychosis, without care takers noticing. In Class 3 (N = 1,747, 47.3%) perplexity, paranoia and negative symptoms were more prevalent. Patients were more often at high risk of developing psychosis. Class 4 (N = 286, 7.7%) represented the 'normative' group with low probabilities for all items. DISCUSSION: The results support the hypothesis that a representation in four classes of psychotic-like experiences can also be applied in a help-seeking population.

Age related differences in symptom networks of overall psychological functioning in a sample of patients diagnosed with anxiety, obsessive compulsive disorder, or posttraumatic stress disorder.

Anxiety disorders, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are among the most prevalent mental disorders across the lifespan. Yet, it has been suggested that there are phenomenological differences and differences in treatment outcomes between younger and older adults. There is, however, no consensus about the age that differentiates younger adults from older adults. As such, studies use different cut-off ages that are not well founded theoretically nor empirically. Network tree analysis was used to identify at what age adults differed in their symptom network of psychological functioning in a sample of Dutch patients diagnosed with anxiety disorders, OCD, or PTSD (N = 27,386). The networktree algorithm found a first optimal split at age 30 and a second split at age 50. Results suggest that differences in symptom networks emerge around 30 and 50 years of age, but that the core symptoms related to anxiety remain stable across age. If our results will be replicated in future studies, our study may suggest using the age split of 30 or 50 years in studies that aim to investigate differences across the lifespan. In addition, our study may suggest that age-related central symptoms are an important focus during treatment monitoring.

Nanoplastics causes extensive congenital malformations during embryonic development by passively targeting neural crest cells.

Nanomaterials are widespread in the human environment as pollutants, and are being actively developed for use in human medicine. We have investigated how the size and dose of polystyrene nanoparticles affects malformations in chicken embryos, and have characterized the mechanisms by which they interfere with normal development. We find that nanoplastics can cross the embryonic gut wall. When injected into the vitelline vein, nanoplastics become distributed in the circulation to multiple organs. We find that the exposure of embryos to polystyrene nanoparticles produces malformations that are far more serious and extensive than has been previously reported. These malformations include major congenital heart defects that impair cardiac function. We show that the mechanism of toxicity is the selective binding of polystyrene nanoplastics nanoparticles to neural crest cells, leading to the death and impaired migration of those cells. Consistent with our new model, most of the malformations seen in this study are in organs that depend for their normal development on neural crest cells. These results are a matter of concern given the large and growing burden of nanoplastics in the environment. Our findings suggest that nanoplastics may pose a health risk to the developing embryo.

Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Measurement invariance of the Center for Epidemiological Studies Depression Scale (CES-D) among Chinese and Dutch elderly.

BACKGROUND: Although previous studies using non- elderly groups have assessed the factorial invariance of the Center for Epidemiological Studies Depression Scale (CES-D) across different groups with the same social-cultural backgrounds, few studies have tested the factorial invariance of the CES-D across two elderly groups from countries with different social cultures. The purposes of this study were to examine the factorial structure of the CES-D, and test its measurement invariance across two different national elderly populations. METHODS: A total of 6806 elderly adults from China (n = 4903) and the Netherlands (n = 1903) were included in the final sample. The CES-D was assessed in both samples. Three strategies were used in the data analysis procedure. First, a confirmatory factor analysis (CFA) was carried out to determine the factor structures of the CES-D that best fitted the two samples. Second, the best fitting model was incorporated into a multi-group CFA model to test measurement invariance of the CES-D across the two population groups. Third, latent mean differences between the two groups were tested. RESULTS: The results of confirmatory factor analysis (CFA) showed: 1) in both samples, Radloff's four-factor model resulted in a significantly better fit and the four dimensions (somatic complaints, depressed affect, positive affect, and interpersonal problems) of the CES-D seem to be the most informative in assessing depressive symptoms compared to the single-, three-, and the second-order factor models; and 2) the factorial structure was invariant across the populations under study. However, only partial scalar and uniqueness invariance of the CES-D items was supported. Latent means in the partial invariant model were lower for the Dutch sample, compared to the Chinese sample. CONCLUSIONS: Our findings provide evidence of a valid factorial structure of the CES-D that could be applied to elderly populations from both China and the Netherlands, producing a meaningful comparison of total scores between the two elderly groups. However, for some specific factors and items, caution is required when comparing the depressive symptoms between Chinese and Dutch elderly groups.

The promise of machine learning in predicting treatment outcomes in psychiatry.

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Depressive and anxiety disorders and the association with obesity, physical, and social activities.

OBJECTIVE: There is evidence of more obesity among persons with depressive and depressive and anxiety disorders. However, the nature and the underlying mechanisms of the association are still unclear. This study examines the association between depressive and anxiety disorders and obesity, physical activity, and social activity, and examines whether social and physical activity are potential influencing factors in the association between depressive and anxiety disorders and obesity. METHOD: Cross-sectional data were used from the Netherlands Study of Depression and Anxiety. A total of 1,854 women and 955 men aged 18-65 years were recruited from the community, general practices, and specialized mental health care. Depressive and anxiety disorders were determined with the Composite International Diagnostic Interview. Body mass index (BMI<30 kg/m(2) ) was used to determine obesity. Physical and social activities were measured by self-report. RESULTS: The odds of obesity adjusted for covariates was significantly higher among those with a current pure Major Depressive Disorder (MDD;odds ratio [OR] OR:1.43; 95% CI:1.07-1.92) compared to controls. Physical activity and social activities were lower among persons with depressive and anxiety disorders compared to controls. The association between MDD and obesity was influenced by social and physical activities. CONCLUSION: This study confirmed a link between depressive disorders and obesity that was influenced by lower social and physical activities among the depressed.

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive performance in many situations. The current article introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A randomized controlled trial of an Internet-based intervention for eating disorders and the added value of expert-patient support: study protocol.

BACKGROUND: E-mental health has become increasingly popular in interventions for individuals with eating disorders (EDs). It has the potential to offer low-threshold interventions and guide individuals to the needed care more promptly. Featback is such an Internet-based intervention and consists of psychoeducation and a fully automated monitoring and feedback system. Preliminary findings suggest Featback to be (cost-)effective in reducing ED symptomatology. Additionally, e-mail or chat support by a psychologist did not enhance the effectiveness of Featback. Support by an expert patient (someone with a lived experience of an ED) might be more effective, since that person can effectively model healthy behavior and enhance self-efficacy in individuals struggling with an ED. The present study aims to replicate and build on earlier findings by further investigating the (cost-)effectiveness of Featback and the added value of expert-patient support. METHODS: The study will be a randomized controlled trial with a two-by-two factorial design with repeated measures. The four conditions will be (1) Featback, in which participants receive automated feedback on a short monitoring questionnaire weekly, (2) Featback with weekly e-mail or chat support from an expert patient, (3) weekly support from an expert patient, and (4) a waiting list. Participants who are 16 years or older and have at least mild self-reported ED symptoms receive a baseline measure. Subsequently, they are randomized to one of the four conditions for 8 weeks. Participants will be assessed again post-intervention and at 3, 6, 9, and 12 months follow-up. The primary outcome measure will be ED psychopathology. Secondary outcome measures are experienced social support, self-efficacy, symptoms of anxiety and depression, user satisfaction, intervention usage, and help-seeking attitudes and behaviors. DISCUSSION: The current study is the first to investigate e-mental health in combination with expert-patient support for EDs and will add to the optimization of the delivery of Internet-based interventions and expert-patient support. TRIAL REGISTRATION: Netherlands Trial Register, NTR7065 . Registered on 7 June 2018.

Comparing factor structures of depressed patients with and without suicidal ideation, a measurement invariance analysis.

BACKGROUND: Suicidality could be associated with specific combinations of biological, social and psychological factors. Therefore, depressive episodes with suicidal ideation could be different from depressive episodes without suicidal ideation in terms of latent variable structures. METHODS: In this study we compared latent variable structures between suicidal and non-suicidal depressed patients using confirmatory factor analysis (CFA), following a measurement invariance test procedure. Patients (N = 919) suffering from major depressive disorder were selected from the Netherlands Study of Depression and Anxiety (NESDA) and split into a group that showed no symptoms of suicidal ideation (non-SI; N = 691) and a suicidal ideation (SI) group that had one or more symptoms of suicidal ideation (N = 228). Depression and anxiety symptoms were measured using the short form of the Mood and Anxiety Symptoms Questionnaire (MASQ-D30). RESULTS: CFA implied a difference in latent variable structures between the non-SI sample (CFI 0.957; RMSEA 0.041) and the SI sample (CFI 0.900; RMSEA 0.056). Subsequent multiple-group CFA showed violations of measurement invariance. The General distress and Anhedonic depression subscales were best indicated by hopelessness and lack of optimism in the SI sample and by dissatisfaction and not feeling lively in the non-SI sample. Overall, the SI sample had higher scores and lower inter-item correlations on the Anhedonic depression items. LIMITATIONS: We have included very mild cases of suicidal ideation in our SI sample. CONCLUSIONS: On a latent variable level, depression with suicidal ideation differs from depression without suicidal ideation. Results encourage further research into the symptom structure of depression among suicidal patients.

Which patients benefit specifically from short-term psychodynamic psychotherapy (STPP) for depression? Study protocol of a systematic review and meta-analysis of individual participant data.

INTRODUCTION: Short-term psychodynamic psychotherapy (STPP) is an empirically supported treatment that is often used to treat depression. However, it is largely unclear if certain subgroups of depressed patients can benefit specifically from this treatment method. We describe the protocol for a systematic review and meta-analysis of individual participant data (IPD) aimed at identifying predictors and moderators of STPP for depression efficacy. METHOD AND ANALYSIS: We will conduct a systematic literature search in multiple bibliographic databases (PubMed, PsycINFO, Embase.com, Web of Science and Cochrane's Central Register of Controlled Trials), 'grey literature' databases (GLIN and UMI ProQuest) and a prospective trial register (http://www.controlled-trials.com). We will include studies reporting (a) outcomes on standardised measures of (b) depressed (c) adult patients (d) receiving STPP. We will next invite the authors of these studies to share the participant-level data of their trials and combine these data to conduct IPD meta-analyses. The primary outcome for this study is post-treatment efficacy as assessed by a continuous depression measure. Potential predictors and moderators include all sociodemographic variables, clinical variables and psychological patient characteristics that are measured before the start of treatment and are assessed consistently across studies. One-stage IPD meta-analyses will be conducted using mixed-effects models. ETHICS AND DISSEMINATION: Institutional review board approval is not required for this study. We intend to submit reports of the outcomes of this study for publication to international peer-reviewed journals in the fields of psychiatry or clinical psychology. We also intend to present the outcomes at international scientific conferences aimed at psychotherapy researchers and clinicians. The findings of this study can have important clinical implications, as they can inform expectations of STPP efficacy for individual patients, and help to make an informed choice concerning the best treatment option for a given patient. PROSPERO REGISTRATION NUMBER: CRD42017056029.