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The Hierarchical Taxonomy of Psychopathology (HiTOP) is a classification system that seeks to organize psychopathology using quantitative evidence – yet the current model was established by narrative review. This meta-analysis provides a quantitative synthesis of literature on transdiagnostic dimensions of psychopathology to evaluate the validity of the HiTOP framework.
Published studies estimating factor-analytic models from diagnostic and statistical manual of mental disorders (DSM) diagnoses were screened. A total of 120,596 participants from 35 studies assessing 23 DSM diagnoses were included in the meta-analytic models. Data were pooled into a meta-analytic correlation matrix using a random effects model. Exploratory factor analyses were conducted using the pooled correlation matrix. A hierarchical structure was estimated by extracting one to five factors representing levels of the HiTOP framework, then calculating congruence coefficients between factors at sequential levels.
Five transdiagnostic dimensions fit the DSM diagnoses well (comparative fit index = 0.92, root mean square error of approximation = 0.07, and standardized root-mean-square residual = 0.03). Most diagnoses had factor loadings >|0.30| on the expected factors, and congruence coefficients between factors indicated a hierarchical structure consistent with the HiTOP framework.
A model closely resembling the HiTOP framework fit the data well and placement of DSM diagnoses within transdiagnostic dimensions were largely confirmed, supporting it as valid structure for conceptualizing and organizing psychopathology. Results also suggest transdiagnostic research should (1) use traits, narrow symptoms, and dimensional measures of psychopathology instead of DSM diagnoses, (2) assess a broader array of constructs, and (3) increase focus on understudied pathologies.
Suicide rates are high in borderline personality disorder (BPD) where interpersonal problems trigger intense affective dysregulation and impulses to act on suicidal thoughts. To date, however, no study has examined how interpersonal stressors contribute to momentary within-person links among affect and impulsivity with suicidal ideation (SI), and how those links vary over time in people's daily lives.
A total of 153 individuals diagnosed with BPD and 52 healthy controls completed a 21-day ecological momentary assessment protocol. Of these 153 individuals with BPD, 105 had a history of suicide attempts. Multilevel structural equation modeling was used to examine dynamic links among interpersonal perceptions, affect, state impulsivity, and suicidal intent.
Aggregated across interactions, lower perceived warmth in others was associated with SI. This direct relationship, however, did not extend to momentary within-person associations. Instead, interpersonal conflicts were linked to SI indirectly via greater negative affect and lower positive affect. While a robust within-person link between interpersonal perceptions and impulsivity emerged, impulsivity did not account for the relationship between interpersonal perceptions and SI.
This intensive longitudinal study illustrates momentary interpersonal signatures of an emerging suicidal crisis. Among people with BPD at high risk for suicide, interpersonal triggers initiate a cascade of affective dysregulation, which in turn gives rise to SI.
In their chapter, Bach and Presnall-Shvorin (this volume) introduce guidelines for incorporating empirically-driven trait models of personality pathology, codified in the DSM-5 and ICD-11, into therapeutic practice. Though the authors of this commentary are supportive of the effort to bridge research with clinical practice, they suggest that a mechanistic model which accounts for personality processes underlying descriptive traits could offer greater precision than traits alone. Furthermore, they argue that clinical dysfunction can only be meaningfully defined and treated with an understanding of dynamic, contextualized aspects of personality. To illustrate how a mechanistic model could complement and extend Bach and Presnall’s recommendations, the authors present a case conceptualization using cybernetic theory. Finally, they review how idiographic data gleaned from ambulatory assessment methods provide insight into pathological processes ideal for therapeutic intervention. To achieve a generalizable approach flexible enough to adapt to the individual, they encourage the development of treatment models that go beyond traits to mechanistically link stable and dynamic personality features into a unified framework.
Most of what clinical psychology concerns itself with is directly unobservable. Concepts like neuroticism and depression, but also learning and development, represent dispositions, states, or processes that must be inferred and cannot (currently) be directly measured. Latent variable modeling, as a statistical framework, encompasses a range of techniques that involve estimating the presence and effect of unobserved variables from observed data. This chapter provides a nontechnical overview of latent variable modeling in clinical psychology. Dimensional latent variable models are emphasized, although categorical and hybrid models are touched on briefly. Challenges with specific models, such as the bifactor model are discussed. Examples draw from the psychopathology literature.
The scientific discipline of clinical psychology has witnessed paradigm changes in the prevailing conceptualization of psychopathology and in the rigor of experimental methods to test psychosocial treatments. In parallel, neuroscience approaches to mental illness have become increasingly prominent and technologies to measure psychological constructs over time and across contexts are becoming ubiquitous in psychological research. Altogether, these changes have pushed clinical scientists to incorporate novel research methodologies and analytic approaches. Modern studies of clinical phenomena are often theoretically integrative and assess constructs across levels of measurement, ranging from the molecular to the behavioral. These shifts are fundamental, and necessitate changes in the way modern clinical psychologists design studies, collect data, and draw scientific conclusions. This book is intended to serve as a guide for the next generation of clinical psychologists, who will benefit from greater training in statistics, study design, developmental psychopathology, and multimethod approaches.
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.