Response shifts in mental health interventions: An illustration of longitudinal measurement invariance.

The efficacy of treatments for depression is often measured by comparing observed total scores on self-report inventories, in both clinical practice and research. However, the occurrence of response shifts (changes in subjects' values, or their standards for measurement) may limit the validity of such comparisons. As most psychological treatments for depression are aimed at changing patients' values and frame of reference, response shifts are likely to occur over the course of such treatments. In this article, we tested whether response shifts occurred over the course of treatment in an influential randomized clinical trial. Using confirmatory factor analysis, measurement models underlying item scores on the Beck Depression Inventory (Beck & Beamesderfer, 1974) of the National Institute of Mental Health Treatment of Depression Collaborative Research Program (Elkin, Parloff, Hadley, & Autry, 1985) were analyzed. Compared with before treatment, after-treatment item scores appeared to overestimate depressive symptomatology, measurement errors were smaller, and correlations between constructs were stronger. These findings indicate a response shift, in the sense that participants seem to get better at assessing their level of depressive symptomatology. Comparing measurement models of patients receiving psychotherapy and medication suggested that the aforementioned effects were more apparent in the psychotherapy groups. Consequently, comparisons of observed total scores on self-report inventories may yield confounded measures of treatment efficacy. © 2013 American Psychological Association

Age and Gender Identity in the Relationship Between Minority Stress and Loneliness:A Global Sample of Sexual and Gender Minority Adults

Loneliness is prevalent among sexual minority adults and is associated with minority stress. Yet there is limited understanding of how loneliness and minority stress vary across key demographic variables. This cross-sectional study explored age and gender differences in a minority stress model linking sexual orientation marginalization to social and emotional loneliness via proximal stress (internalized homonegativity, concealment, and stigma preoccupation) and via social anxiety and inhibition. The study also assessed age and gender differences in the protective influence of LGBTQ community involvement. 7,856 sexual minority adults from 85 countries completed an online survey. They were categorized as emerging adults (18−24, n = 3,056), young adults (25−34, n = 2,193), midlife adults (35−49, n = 1,243), and older adults (50−88, n = 1,364). Gender identity groups were cisgender men (n = 4,073), cisgender women (n = 3,017), and transgender individuals (n = 766). With each successive age group, there was a lower prevalence of sexual orientation marginalization, proximal stress, social anxiety, inhibition, and emotional loneliness, along with more community involvement. Sexual orientation marginalization was more pronounced among cisgender women and, especially, transgender individuals. The latter also exhibited the most social anxiety, inhibition, loneliness, and community involvement. Proximal stress was more prevalent among cisgender men than cisgender women and transgender individuals. Multiple group structural equation modeling supported the applicability of the loneliness model across age and gender groups, with only a few variations; these mainly related to how strongly community involvement was linked to marginalization, internalized homonegativity, and social loneliness.</p

Age and Gender Identity in the Relationship Between Minority Stress and Loneliness:A Global Sample of Sexual and Gender Minority Adults

Loneliness is prevalent among sexual minority adults and is associated with minority stress. Yet there is limited understanding of how loneliness and minority stress vary across key demographic variables. This cross-sectional study explored age and gender differences in a minority stress model linking sexual orientation marginalization to social and emotional loneliness via proximal stress (internalized homonegativity, concealment, and stigma preoccupation) and via social anxiety and inhibition. The study also assessed age and gender differences in the protective influence of LGBTQ community involvement. 7,856 sexual minority adults from 85 countries completed an online survey. They were categorized as emerging adults (18−24, n = 3,056), young adults (25−34, n = 2,193), midlife adults (35−49, n = 1,243), and older adults (50−88, n = 1,364). Gender identity groups were cisgender men (n = 4,073), cisgender women (n = 3,017), and transgender individuals (n = 766). With each successive age group, there was a lower prevalence of sexual orientation marginalization, proximal stress, social anxiety, inhibition, and emotional loneliness, along with more community involvement. Sexual orientation marginalization was more pronounced among cisgender women and, especially, transgender individuals. The latter also exhibited the most social anxiety, inhibition, loneliness, and community involvement. Proximal stress was more prevalent among cisgender men than cisgender women and transgender individuals. Multiple group structural equation modeling supported the applicability of the loneliness model across age and gender groups, with only a few variations; these mainly related to how strongly community involvement was linked to marginalization, internalized homonegativity, and social loneliness.</p

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi

Improved prediction rule ensembling through model-based data generation

Multivariate analysis of psychological dat

Improved prediction rule ensembling through model-based data generation

Multivariate analysis of psychological dat