Co-occurrence of psychiatric disorders is welldocumented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the 'general psychopathology' or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are wellsuited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data. 1 Sometimes, group factors are called "specific factors." However, "specific factor" more correctly refers to an item's reliable (non-error) variance that is not shared with other items (5).

For decades, clinicians and researchers have recognized that borderline personality disorder (BPD) and substance use disorders (SUDs) are often diagnosed within the same person (e.g., (Gunderson JG. Borderline personality disorder: A clinical guide. Washington, D.C.: American Psychiatric Press, 2001; Leichsenring et al., Lancet 377:74-84, 2011; Paris J. Borderline personality disorder: A multidimensional approach. American Psychiatric Pub, 1994; Trull et al., Clin Psychol Rev 20:235-53, 2000)). Previously, we documented the extent of this co-occurrence and offered a number of methodological and theoretical explanations for the co-occurrence (Trull et al., Clin Psychol Rev 20:235-53, 2000). Here, we provide an updated review of the literature on the co-occurrence between borderline personality disorder (BPD) and substance use disorders (SUDs) from 70 studies published from 2000 to 2017, and we compare the co-occurrence of these disorders to that documented by a previous review of 36 studies over 15 years ago (Trull et al., Clin Psychol Rev 20:235-53, 2000).

Co-occurrence of psychiatric disorders is well-documented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the ‘general psychopathology’ or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are well-suited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data.

Considerable attention has been directed towards studying co-occurring psychopathology through the lens of a general factor (p-factor). However, the developmental trajectory and stability of the p-factor have yet to be fully understood. The present study examined the explanatory power of dynamic mutualism theory – an alternative framework that suggests the p-factor is a product of lower-level symptom interactions that strengthen throughout development. Data were drawn from a population-based sample of girls (N = 2450) who reported on the severity of internalizing and externalizing problems each year from age 14 to age 21. Predictions of dynamic mutualism were tested using three distinct complementary statistical approaches including: longitudinal bifactor models, random-intercept cross-lagged panel models (RI-CLPMs), and network models. Across methods, study results document preliminary support for mutualistic processes in the development of co-occurring psychopathology (that is captured in p). Findings emphasize the importance of exploring alternative frameworks and methods for better understanding the p-factor and its development.

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