Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder.

BACKGROUND: International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS: To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD: Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS: Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS: The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling.

In the present article, we discuss the role that quantitative genetic methodology may play in assessing and understanding the dimensionality of psychological (psychometric) instruments. Specifically, we study the relationship between the observed covariance structures, on the one hand, and the underlying genetic and environmental influences giving rise to such structures, on the other. We note that this relationship may be such that it hampers obtaining a clear estimate of dimensionality using standard tools for dimensionality assessment alone. One situation in which dimensionality assessment may be impeded is that in which genetic and environmental influences, of which the observed covariance structure is a function, differ from each other in structure and dimensionality. We demonstrate that in such situations settling dimensionality issues may be problematic, and propose using quantitative genetic modeling to uncover the (possibly different) dimensionalities of the underlying genetic and environmental structures. We illustrate using simulations and an empirical example on childhood internalizing problems.

Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences.

This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro versus contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed, and person fit in IRT. We discuss the relation between this model and existing models as well as implications for psychology and psychometrics.