ORCID Profile
0000-0002-1910-223X
Current Organisation
Stony Brook University
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Publisher: SAGE Publications
Date: 07-03-2019
Abstract: For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across erse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
Publisher: Cambridge University Press (CUP)
Date: 02-06-2022
DOI: 10.1017/S0033291722001301
Abstract: The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.
Publisher: American Psychological Association (APA)
Date: 10-2019
DOI: 10.1037/ABN0000434
Abstract: Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Publisher: SAGE Publications
Date: 25-10-2021
DOI: 10.1177/21677026211049390
Abstract: In this commentary, we discuss questions and misconceptions about the Hierarchical Taxonomy of Psychopathology (HiTOP) raised by Haeffel et al. We explain what the system classifies and why it is descriptive and atheoretical, and we highlight benefits and limitations of this approach. We clarify why the system is organized according to patterns of covariation or comorbidity among signs and symptoms of psychopathology, and we discuss how it is designed to be falsifiable and revised in a manner that is responsive to data. We refer to the body of evidence for HiTOP’s external validity and for its scientific and clinical utility. We further describe how the system is currently used in clinics. In sum, many of Haeffel et al.’s concerns about HiTOP are unwarranted, and for those concerns that reflect real current limitations of HiTOP, our consortium is working to address them, with the aim of creating a nosology that is comprehensive and useful to both scientists and clinicians.
Publisher: American Psychological Association (APA)
Date: 02-2020
DOI: 10.1037/ABN0000486
Publisher: Center for Open Science
Date: 28-10-2021
Abstract: This commentary discusses questions and misconceptions about HiTOP raised by Haeffel et al. (2021). We explain what the system classifies and why it is descriptive and atheoretical, highlighting benefits and limitations of this approach. We clarify why the system is organized according to patterns of covariation or comorbidity among signs and symptoms of psychopathology, and we discuss how it is designed to be falsifiable and revised in a manner that is responsive to data. We refer to the body of evidence for HiTOP’s external validity and for its scientific and clinical utility. We further describe how the system is currently used in clinics. In sum, many of Haeffel et al.’s concerns about HiTOP are unwarranted, and for those concerns that reflect real current limitations of HiTOP, our consortium is working to address them, with the aim of creating a nosology that is comprehensive and useful to both scientists and clinicians.
Publisher: Wiley
Date: 18-05-2021
DOI: 10.1002/WPS.20844
Publisher: Oxford University Press (OUP)
Date: 16-05-2018
Publisher: Wiley
Date: 07-09-2018
DOI: 10.1002/WPS.20566
Publisher: Wiley
Date: 11-05-2020
DOI: 10.1002/WPS.20730
No related grants have been discovered for Katherine Jonas.